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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Open accessAbstract
Background: To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F 1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results: The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions: In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F 1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F 1 score in evaluating binary classification tasks by all scientific communities.
Overview
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TL;DR
The paper argues that the Matthews correlation coefficient (MCC) is a more reliable metric than accuracy and F1 score for evaluating binary classifications, especially on imbalanced datasets, because MCC requires good performance on both positive and negative classes. It supports this through mathematical analysis, six synthetic use cases, and a real colon cancer gene expression experiment.
Key findings
- In six synthetic use cases, MCC was the only metric that correctly indicated the prediction problem in all six. For Use case A1 (91 positives, 9 negatives; TP=90, FN=1, TN=0, FP=9), accuracy=0.90 and F1=0.95, but MCC=−0.03. For Use case C2 (11 positives, 89 negatives; TP=2, FN=9, TN=88, FP=1), accuracy=0.90, F1=0.29, and MCC=+0.31.
- Correlation analysis over all possible confusion matrices showed MCC and accuracy were strongly correlated, while F1 correlated less than 0.8 with the other two measures. For N=1000, PCC(MCC,F1)=0.775982, PCC(MCC,accuracy)=0.921030, and PCC(accuracy,F1)=0.778652.
- In the colon cancer gene expression dataset (2,000 gene probesets, 62 patients: 22 healthy controls and 40 colon cancer; 35.48% negatives, 64.52% positives), rankings differed by metric. MCC ranked gradient boosting top (MCC=+0.55, TP rate=0.85, TN rate=0.69), while F1 and accuracy ranked k-NN top (F1=0.87, accuracy=0.80, TP rate=0.92, TN rate=0.52). Radial SVM had F1=0.75 and accuracy=0.67 but TN rate=0.40 and MCC=+0.29.
- MCC produces a high score only if the classifier correctly predicts the majority of positive and negative instances; F1 is independent of true negatives and is not symmetric for class swapping, while accuracy can be overoptimistic on imbalanced datasets.
Methods
- The paper defines accuracy, MCC, and F1 score mathematically, explores their relationships using Pearson correlation coefficient over all possible confusion matrices for N≥10 up to 1000, and plots all 21,084,251 possible confusion matrices for a 500-sample dataset on the MCC/F1 plane.
- Six synthetic use cases vary class balance and imbalance with specified confusion matrices to compare metric behavior.
- The real genomics experiment uses colon cancer gene expression data from Alon et al. via the plsgenomics R package, applying gradient boosting, decision tree, k-nearest neighbors, linear SVM, and radial SVM. Training/test splits were 80/20 or 60/20/20 with validation for hyperparameter grid search; experiments were repeated ten times and averaged for MCC, F1, accuracy, TP rate, and TN rate.
Limitations
- The paper states that additional comparisons with other rates such as Cohen’s Kappa, Cramér’s V, and K measure would have provided further information, but the authors focused on accuracy and F1 because they are more commonly used in biomedical machine learning studies.
- The paper notes that MCC can be undefined when a whole row or column of the confusion matrix is zero, and handles these cases via an approximation using an arbitrarily small value ε; it also cites situations where MCC can display large fluctuations due to imbalanced outcomes.
- Evident from the text: the real-world validation uses a single colon cancer gene expression dataset, the six use cases are synthetic, and Table 5 excludes standard deviations to keep the table simple.
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![Fig. 2 Use case A1 — Positively imbalanced dataset. a Barplot representing accuracy, F1, and normalized Matthews correlation coefficient (normMCC = (MCC + 1) / 2), all in the [0, 1] interval, where 0 is the worst possible score and 1 is the best possible score, applied to the Use case A1 positively imbalanced dataset. b Pie chart representing the amounts of true positives (TP), false negatives (FN), true negatives (TN), and false positives (FP). c Pie chart representing the dataset balance, as the amounts of positive data instances and negative data instances](/paper-figures/988371a13eb649a5bff767f781052a4f/the-advantages-of-the-matthews-correlation-coefficient-mcc-over-f1-score-and-figure-2.jpg)
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29 Own interpretation · 28 Own result · 15 Not checked yet · 13 No basis given
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In the paper
“A potential problem with MCC lies in the fact that MCC is undefined when a whole row or column of M is zero, as it happens in the previously cited case of the trivial majority classifier.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
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“Second, F1 score is independent from the number of samples correctly classified as negative.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
plain- Asserted: passes — The authors put it forward as true.
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“she/he would notice good results (F1 score = 0.75 and accuracy = 0.67)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
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“For the Use case A1 (negatively imbalanced dataset), the machine learning classifier was unable to correctly predict negative data instances, and it therefore produced confusion matrices featuring few true negatives (TN).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“First, the top classifier changes when we consider the ranking based on MCC, F1 score, or accuracy.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
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“MCC = +0.31 in the [–1, +1] interval”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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In the paper
“In the Use case B1 (balanced dataset), the machine learning method was unable to correctly predict negative data instances, and therefore produced a confusion matrix featuring few true negatives (TN).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“Finally, in the last Use case C2 (positively imbalanced dataset), the prediction technique failed in predicting negative elements, and therefore its confusion matrix showed a low percentage of true negatives.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“F1 score = 0.29 in the [0, 1] interval”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“the radial SVM has performed poorly on the true negatives (TN rate = 0.40), by correctly predicting less than half patients.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
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“Here, accuracy showed a high value: 0.90 in the [0, 1] interval.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“In this case, all three statistical rates showed a low score which emphasized the deficiency in the prediction process (accuracy = 0.24, F1 score = 0.12, and MCC = −0.24).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“It ranges in the interval [−1,+1], with extreme values –1 and +1 reached in case of perfect misclassification and perfect classification, respectively, while MCC=0 is the expected value for the coin tossing classifier.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
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“F1 ranges in [0,1], where the minimum is reached for TP=0, that is, when all the positive samples are misclassified, and the maximum for FN=FP=0, that is for perfect classification.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
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“Like for the Use case A2, accuracy, F1 and MCC show average or low result scores (accuracy = 0.56, F1 score = 0.31, and MCC = +0.17), correctly informing you about the non-optimal performance of the prediction method (Additional file 3a).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“If M has only one non-zero entry, this means that all samples in the dataset belong to one class, and they are either all correctly (for TP≠0 or TN≠0) or incorrectly (for FP≠0 or FN≠0) classified. In this situations, MCC=1 for the former case and MCC=−1 for the latter case.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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“If the dataset is highly unbalanced, ni≈1, and thus the accuracy measure gives an unreliable estimation of the goodness of the classifier.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
plaindirect- Asserted: passes — The authors put it forward as true.
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“Checking only F1, one would read a good value (0.66 in the [0, 1] interval), and would be overall satisfied about the prediction (Additional file 2a).”
- Also stated in: § Use cases · hedged
“The Matthews correlation coefficient, instead, by showing a score close to random guessing (+0.07 in the [–1, +1] interval) would be able to inform that the machine learning method has been on the wrong track. Also, it is worth noticing that accuracy would provide with an informative result in this case (0.52 in the [0, 1] interval).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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- Also stated in: § Use cases · hedged
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“However, accuracy and of F1 showed high values in this case: accuracy = 0.90 and F1 score = 0.95, both close to the best possible value 1.00 in the [0, 1] interval (Fig. 2a).”
- Also stated in: § Use cases · hedged
“Instead, if one decided to take advantage of the Matthews correlation coefficient in the Use case A1, he/she would notice the resulting MCC = –0.03 (Fig. 2a).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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- Also stated in: § Use cases · hedged
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“Similar to the Use case A2 and B2, all three statistical scores generated low results that reflect the mediocre quality of the prediction: F1 score = 0.17 and accuracy = 0.10 in the [0, 1] interval, and MCC = −0.19 in the [–1, +1] interval (Additional file 4a).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plainSingle case- Asserted: passes — The authors put it forward as true.
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“On the other hand, for a given value MCC=μ, the F1 score can range in [0,μ+1] if μ≤0 and in [μ,1] if μ>0, so that the width of the range is 1−|μ|, that is, it depends on the MCC value μ.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The two measures are reasonably concordant, but the scatterplot cloud is wide, implying that for each value of F1 score there is a corresponding range of values of MCC and vice versa, although with different width.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Overall, accuracy, F1, and MCC show reliable concordant scores for predictions that correctly classify both positives and negatives (having therefore many TP and TN), and for predictions that incorrectly classify both positives and negatives (having therefore few TP and TN); however, these measures show discordant behaviors when the prediction performs well just with one of the two binary classes.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“First, F1 varies for class swapping, while MCC is invariant if the positive class is renamed negative and vice versa.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Interestingly, the correlation grows with N, but the increments are limited.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“With these positions MCC is now defined for all confusion matrices M. As a consequences, MCC=0 for the trivial majority classifier, and 0 is also the expected value for the coin tossing classifier.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
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In the paper
“MCC and accuracy resulted strongly correlated, while the Pearson coefficient is less than 0.8 for the correlation of F1 with the other two measures (Table 3).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“In fact, for any value F1=ϕ, the MCC varies approximately between [ϕ−1,ϕ], so that the width of the variability range is 1, independent from the value of ϕ.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The micro/macro averaged F1 is invariant for class swapping and its behaviour is more similar to MCC.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Here, accuracy showed a high value: 0.90 in the [0, 1] interval.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“linear SVM is ranked forth in the MCC ranking and in the accuracy ranking, but ranked second in the F1 score ranking.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
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In the paper
“k-NN, that is the top performing method both in the F1 score ranking and in the accuracy ranking, instead, obtained an excellent score for recall (TP rate = 0.92) but just sufficient on the specificity (TN rate = 0.52).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“However, ROC AUC presents several flaws [92], and it is sensitive to class imbalance [93].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [92] Lobo JM, Jiménez-Valverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr. 2008; 17(2):145–51. source
- Not checked yetcites [93] Hanczar B, Hua J, Sima C, Weinstein J, Bittner M, Dougherty ER. Small-sample precision of ROC-related estimates. Bioinformatics. 2010; 26(6):822–30. source
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In the paper
“Once again, this score would hide the truth: the classification algorithm has performed poorly on the negative subset.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
hedgeddirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one. The quote only partly states this claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The effectiveness of MCC has been shown in other scientific fields as well [43, 44].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [43] Liu Y, Cheng J, Yan C, Wu X, Chen F. Research on the Matthews correlation coefficients metrics of personalized recommendation algorithm evaluation. Int J Hybrid Informa Technol. 2015; 8(1):163–72. source
- Not checked yetcites [44] Naulaerts S, Dang CC, Ballester PJ. Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours. Oncotarget. 2017; 8(57):97025. source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Even if less common than the ROC curve, several scientists consider the PR curve more informative than the ROC curve, especially on imbalanced biological and medical datasets [48, 97, 98].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [48] Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. 2015; 10(3):0118432. source
- Not checked yetcites [97] Chicco D. Ten quick tips for machine learning in computational biology. BioData Min. 2017; 10(35):1–17.
- Not checked yetcites [98] Ozenne B, Subtil F, Maucort-Boulch D. The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases. J Clin Epidemiol. 2015; 68(8):855–9. source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“MCC was re-proposed by Baldi and colleagues [39] in 2000 as a standard performance metric for machine learning with a natural extension to the multiclass case [40].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [39] Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics. 2000; 16(5):412–24. source
- Not checked yetcites [40] Gorodkin J. Comparing two K-category assignments by a K-category correlation coefficient. Comput Biol Chem. 2004; 28(5–6):367–74. source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, when Whalen and colleagues released TargetFinder, a tool to predict enhancer-promoters interactions in genomics, they showed its results measured only by F1 score [13], making it impossible to detect the actual true positive rate and true negative rate of their tests [69].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [13] Whalen S, Truty RM, Pollard KS. Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat Genet. 2016; 48(5):488. source
- Not checked yetcites [69] Xi W, Beer MA. Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy. PLoS Comput Biol. 2018; 14(12):1006625. source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Although being widely acknowledged as a reliable metric, there are situations - albeit extreme - where either MCC cannot be defined or it displays large fluctuations [45], due to imbalanced outcomes in the classification.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [45] Brown JB. Classifiers and their metrics quantified. Mol Inform. 2018; 37:1700127. source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“An effective solution overcoming the class imbalance issue comes from the Matthews correlation coefficient (MCC), a special case of the ϕ phi coefficient [36].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [36] Guilford JP. Psychometric Methods. New York City: McGraw-Hill; 1954.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“for instance, the Food and Drug Administration (FDA) agency of the USA employed the MCC as the main evaluation measure in the MicroArray II / Sequencing Quality Control (MAQC/SEQC) projects [41, 42].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [41] The MicroArray Quality Control (MAQC) Consortium. The MAQC-II Project: a comprehensive study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol. 2010; 28(8):827–38. source
- Not checked yetcites [42] The SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequence Quality Control consortium. Nat Biotechnol. 2014; 32:903–14. source
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, this procedure is biased [65], and it is still far from being accepted as a standard practice by the community.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [65] Van Asch V. Macro-and micro-averaged evaluation measures. Technical report. 2013:1–27.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this case, the algorithm showed its ability to predict the positive data instances (90 sick patients out of 91 were correctly predicted), but it also displayed its lack of talent in identifying healthy controls (only 1 healthy individual out of 9 was correctly recognized) (Fig. 2b). Therefore, the overall performance should be judged poor.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similar to the Use case A1 and B1, the method was able to correctly identify multiple negative data instances (88 healthy patients out of 89), but unable to correctly predict most of sick patients (only 2 true positives out of 11 possible elements).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plainn = 88- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Originally developed by Matthews in 1975 for comparison of chemical structures [38]”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [38] Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct. 1975; 405(2):442–51. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Despite its popularity, in the learning context there are a number of issues causing the kappa measure to produce unreliable results (for instance, its high sensitivity to the distribution of the marginal totals [73–75])”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [73] Flight L, Julious SA. The disagreeable behaviour of the kappa statistic. Pharm Stat. 2015; 14:74–8. source
- Not checked yetcites [74] Powers DMW. The problem with Kappa. In: Proceedings of EACL 2012 – the 13th Conference of the European Chapter of the Association for Computational Linguistics. Avignon: ACL: 2012. p. 345–55.
- Not checked yetcites [75] Delgado R, Tibau X-A. Why Cohen’s Kappa should be avoided as performance measure in classification. PloS ONE. 2019; 14(9):0222916.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, when a prediction displays many true positives but few true negatives (or many true negatives but few true positives) we will show that F1 and accuracy can provide misleading information, while MCC always generates results that reflect the overall prediction issues.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results show that, while accuracy and F1 score often generate high scores that do not inform the user about ongoing prediction issues, the MCC is a robust, useful, reliable, truthful statistical measure able to correctly reflect the deficiency of any prediction in any dataset.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
boosteddirectn = 6- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here accuracy again generated overoptimistic, misleading, and inflated high results”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The F1 score ranking and the accuracy ranking, in conclusion, are hiding this important flaw of the top classifier: k-NN was unable top correctly predict a high percentage of patients.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There, accuracy and F1 generated overoptimistic and inflated results”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accuracy and F1 score, although popular, can generate misleading results on imbalanced datasets, because they fail to consider the ratio between positive and negative elements.”
- Also stated in: § Conclusions · plain
“F1 and accuracy, instead, generate reliable results only when applied to balanced datasets, and produce misleading results when applied to imbalanced cases.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Conclusions · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With its low value (MCC = +0.29), the MCC informs the readers about the poor general outcome of the radial SVM, while the accuracy and F1 score show misleading values.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The dataset contains 2,000 gene probsets for 62 patients, of which 22 are healthy controls and 40 have colon cancer (35.48% negatives and 64.52% positives) [106].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
What the sources say
- Not checked yetcites [106] Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ. Data pertaining to the article ‘Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays’. 2000. http://genomics-pubs.princeton.edu/oncology/affydata/index.html. Accessed 10 Dec 2019.
plainn = 62- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Accuracy and F1 score are the most common metrics used for binary classification in machine learning.
“We decided to focus on accuracy and F1 score because they are the most common metrics used for binary classification in machine learning.”
The paper relies on this without support.
The synthetic use cases are realistic representations of binary classification scenarios.
“here we describe some synthetic, realistic scenarios where MCC results are more informative and truthful than the other two measures analyzed.”
The paper relies on this without support.
Accuracy and F1 score are more commonly used in machine learning studies related to biomedical applications than other rates.
“because accuracy and F1 score are more commonly used in machine learning studies related to biomedical applications.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“MCC is a more reliable metric, whereby a high score is assigned only if the prediction was correct for the four base categories (TP, FP, TN and FP), taking into account the size of P and N [85].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [85]
In the citing paper
“since it has been shown to present a balanced score when dealing with imbalanced data [11, 6] as is our case with fewer manipulated images.”
From BioFors: A Large Biomedical Image Forensics Dataset (2021) · cites this paper as [11]
In the citing paper
“(3) the absolute value of F1 changes by simply switching the outcome labels (ie, when 1 becomes 0 and 0 becomes 1).”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [77]
In the citing paper
“(2) F1 has no intuitive interpretation”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [77]
In the citing paper
“(1) F1 ignores true negatives”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [77]
In the citing paper
“F1 and MCC were introduced to address the challenges caused by class imbalance, where classification accuracy can be inflated by classifying all patients as low risk when the event is rare.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [77]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Here, accuracy again generated an overoptimistic inflated score”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“all three statistical scores generated low results that reflect the mediocre quality of the prediction: F1 score = 0.17 and accuracy = 0.10 in the [0, 1] interval”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“the prediction technique failed in predicting negative elements, and therefore its confusion matrix showed a low percentage of true negatives”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“the method was able to correctly identify multiple negative data instances (88 healthy patients out of 89), but unable to correctly predict most of sick patients (only 2 true positives out of 11 possible elements)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Even if accuracy showed an excessively high result score”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“all three statistical rates showed a low score which emphasized the deficiency in the prediction process (accuracy = 0.24, F1 score = 0.12, and MCC = −0.24)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
“the best classifier resulted being k-NN (F1 score = 0.87 and accuracy = 0.81)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
“k-nearest neighbors | +0.48 | 0.87 | 0.80 | 0.92 | 0.52”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“only 1 healthy individual out of 9 was correctly recognized”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“TP=90, FN=1, TN=0, FP=9”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Finally, in the last Use case C2 (positively imbalanced dataset)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Use case C2 Negatively imbalanced dataset | 11 | 89 | 2 | 9 | 88 | 1”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“For the Use case A1 (negatively imbalanced dataset)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“made of 9 healthy individuals (negatives =9%) and 91 sick patients (positives =91%)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Also in the Use case C1 (positively imbalanced dataset)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“90 healthy controls (negatives =90%) and 10 sick patients (positives =10%)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“only 5 sick patients out of 70”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“TP = 5, FN = 70, TN = 19, FP = 6”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“the resulting MCC = –0.03”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
“In red, the (−0.04, 0.95) point corresponding to use case A1”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877
Measures of the Amount of Ecologic Association Between Species
Dice, L. R. (1945) · Ecology · doi:10.2307/1932409 · Cited by 12,187
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,051
Research works printed but not linked: 7
- Akosa JS. Predictive accuracy: a misleading performance measure for highly imbalanced data. In: Proceedings of the SAS Global Forum 2017 Conference. Cary, North Carolina: SAS Institute Inc.: 2017. p. 942–2017.
- Guilford JP. Psychometric Methods. New York City: McGraw-Hill; 1954.
- van Rijsbergen CJ, Joost C. Information Retrieval. New York City: Butterworths; 1979.
- Sasaki Y. The truth of the F-measure. Teach Tutor Mater. 2007; 1(5):1–5.
- Dubey A, Tarar S. Evaluation of approximate rank-order clustering using Matthews correlation coefficient. Int J Eng Adv Technol. 2018; 8(2):106–13.
- Suresh Babu N. Various performance measures in binary classification – An overview of ROC study. Int J Innov Sci Eng Technol. 2015; 2(9):596–605.
- Ferri C, Hernández-Orallo J, Flach PA. A coherent interpretation of AUC as a measure of aggregated classification performance. In: Proceedings of ICML 2011 – the 28th International Conference on Machine Learning. Norristown: Omnipress: 2011. p. 657–64.
Cited by
Sorted by citations
Items 101–200 of 1,016
Area under the ROC Curve has the most consistent evaluation for binary classification
Li, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0316019 · Cited by 160
“For example, Matthew’s correlation coefficient (MCC) has been proposed as a better alternative because it has high values only when a model predicts well on both positive and negative classes [7, 8, 12, 34].”
Disease diagnostics using machine learning of B cell and T cell receptor sequences
Zaslavsky, M., Craig, E., Michuda, J., Sehgal, N., Ram-Mohan, N., Lee, J.-Y., Nguyen, K. D., Hoh, R. A., Pham, T. D., Röltgen, K., et al. (2025) · Science · doi:10.1126/science.adp2407 · Cited by 132
Jacobs, P. G., Herrero, P., Facchinetti, A., Vehi, J., Kovatchev, B., Breton, M. D., Cinar, A., Nikita, K. S., Doyle, F. J., Bondia, J., et al. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3331297 · Cited by 113
Chicco, D., Старовойтов, В. В., & Jurman, G. (2021) · IEEE Access · doi:10.1109/access.2021.3068614 · Cited by 112
“The Matthews correlation coefficient (MCC) [1], [3], [5] is the 2× 2 case of φ coefficient [31], and is defined as:”
BackgroundMethodInfluentialSelf-citationFetal ECG Extraction From Maternal ECG Using Attention-Based CycleGAN
Mohebbian, M. R., Vedaei, S. S., Wahid, K. A., Dinh, A., Marateb, H. R., & Tavakolian, K. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3111873 · Cited by 109
Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
Sepúlveda, A., Castillo, F., Palma, C., & Rodriguez-Fernández, M. (2021) · Applied Sciences · doi:10.3390/app11114945 · Cited by 108
“Metrics such as accuracy and F1-score were used in the comparison [21].”
MethodMapping peat thickness and carbon stocks of the central Congo Basin using field data
Crezee, B., Dargie, G. C., Ewango, C. E. N., Mitchard, E. T. A., Ovide Emba, B., Joseph Kanyama, T., Bola, P., Ndjango, J.-B. N., Girkin, N. T., Bocko, Y. E., et al. (2022) · Nature Geoscience · doi:10.1038/s41561-022-00966-7 · Cited by 135
CSM-Toxin: A Web-Server for Predicting Protein Toxicity
Morozov, V., Rodrigues, C. H. M., & Ascher, D. B. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15020431 · Cited by 111
“It has been previously shown to be a more appropriate metric for assessing predictions in unbalanced data, such as the one presented in this study, unlike accuracy, precision and recall [16].”
BackgroundEvaluating reliability in wearable devices for sleep staging
Birrer, V., Elgendi, M., Lambercy, O., & Menon, C. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01016-9 · Cited by 129
Junttila, S., Smolander, J., & Elo, L. L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac286 · Cited by 109
CogEmoNet: A Cognitive-Feature-Augmented Driver Emotion Recognition Model for Smart Cockpit
Li, W., Zeng, G., Zhang, J., Xu, Y. H., Xing, Y., Zhou, R., Guo, G., Shen, Y., Cao, D., & Wang, F. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3127935 · Cited by 104
Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112
Unsupervised Algorithms to Detect Zero-Day Attacks: Strategy and Application
Zoppi, T., Ceccarelli, A., & Bondavalli, A. (2021) · IEEE Access · doi:10.1109/access.2021.3090957 · Cited by 116
Youssef, K. K., Narwade, N. P., Arcas, A., Márquez-Galera, Á., Jiménez-Castaño, R., López-Blau, C., Fazilaty, H., García-Gutierrez, D., Cano, A., Galcerán, J., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00839-5 · Cited by 110
Das, A., Mishra, S. R., & Gopalan, S. S. (2020) · PeerJ · doi:10.7717/peerj.10083 · Cited by 106
Setiawan, A. W. (2020) · CENiM · doi:10.1109/cenim51130.2020.9297970 · Cited by 81
“Moreover, MCC has some advantages over accuracy and DC [14].”
BackgroundA survey on extraction of causal relations from natural language text
Yang, J., Han, S. C., & Poon, J. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01665-w · Cited by 139
“However, they may generate overoptimistic, misleading results on imbalanced datasets, as they failed to consider the ratio between positive and negative classes [16].”
BackgroundShi, Z., Zhang, H., Jin, C., et al. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04073-z · Cited by 102
Movsessian, A., Cava, D. G., & Tcherniak, D. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107766 · Cited by 105
Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine
Mayer, T. J., Poortinga, A., Bhandari, B., Nicolau, A. P., Markert, K. N., Thwal, N. S., Markert, A. M., Haag, A. V., Kilbride, J. B., Chishtie, F., et al. (2021) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2021.100005 · Cited by 103
A novel SMOTE-based resampling technique trough noise detection and the boosting procedure
Sağlam, F., & Cengiz, M. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117023 · Cited by 103
Sujon, K. M., hassan, R. B., Choi, K., & Samad, M. A. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01313-4 · Cited by 114
“Unlike Accuracy, it remains reliable even when class distributions are highly skewed [48].”
Li, H., Zhang, L., & Chen, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac407 · Cited by 100
A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers
Chen, Z., Zhang, J. M., Sarro, F., & Harman, M. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3583561 · Cited by 130
“, the Bank dataset) that we use have an imbalanced class distribution, we follow previous SE work to use three additional macro-average metrics [34, 61, 62] and the Matthews Correlation Coefficient (MCC) metric [65, 73], which are all demonstrated to be suitable for dealing with imbalanced scenarios [38, 68].”
Dina, A. S., Siddique, A. B., & Manivannan, D. (2022) · IEEE Access · doi:10.1109/access.2022.3205337 · Cited by 118
“Next,650 we discuss the results from our experiments.651 F. RESULTS FROM OUR EXPERIMENTS652 1) PERFORMANCE COMPARISON BASED ON THE653 MCC SCORE654 For comparing the performance ofML classifiers,MCC score655 has been shown to be more robust compared to other metrics656 such as F1-score.”
BackgroundInfluentialHargreaves, C. J., Gaultois, M. W., Daniels, L. M., Watts, E. J., Kurlin, V. A., Moran, M. J., Dang, Y., Morris, R. E., Morscher, A., Thompson, K., et al. (2023) · npj Computational Materials · doi:10.1038/s41524-022-00951-z · Cited by 104
XAI-IoT: An Explainable AI Framework for Enhancing Anomaly Detection in IoT Systems
Gummadi, A. N., Napier, J. C., & Abdallah, M. (2024) · IEEE Access · doi:10.1109/access.2024.3402446 · Cited by 107
“…we first create the confusion matrix for each model and use it to derive the following different metrics for each AI model: accuracy (Acc), precision (Prec), recall (Rec), F1-score (F1), Matthews correlation coefficient (Mcc) [62], balanced accuracy (Bacc), and the area under ROC curve (AucRoc).”
MethodEnhancing self-management in type 1 diabetes with wearables and deep learning
Zhu, T., Uduku, C. N., Li, K., Herrero, P., Oliver, N. S., & Georgiou, P. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00626-5 · Cited by 114
Tummala, S., Kim, J., & Kadry, S. (2022) · Mathematics · doi:10.3390/math10214109 · Cited by 106
Nag, S., Makwana, D., Sai Chandra Teja, R., Mittal, S., & Mohan, C. K. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103720 · Cited by 98
“MCC is the only binary classification rate that a high score only if the binary classifier could correctly predict a majority of positive instances and the majority of negative instances [32].”
BackgroundMethodInfluentialThe Evolution of Federated Learning-Based Intrusion Detection and Mitigation: A Survey
Lavaur, L., Pahl, M., Busnel, Y., & Autrel, F. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3177512 · Cited by 110
“MMC has significant advantages over the other metrics, as it covers all four categories of the confusion matrix (see [71]).”
BackgroundWeighted kappa measures for ordinal multi-class classification performance
Yılmaz, A. E., & Demirhan, H. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110020 · Cited by 109
Murphy, C. L., Yang, R., Decker, T., et al. (2021) · Applied and Environmental Microbiology · doi:10.1128/aem.01706-21 · Cited by 93
“Matthew’s Correlation Coefficient (62) was used to quantify classification accuracy.”
MethodIslam, M. M., Hossain, M. B., Akhtar, M. N., Moni, M. A., & Hasan, K. F. (2022) · Algorithms · doi:10.3390/a15080287 · Cited by 100
“Two statistical tests have been carried out, named the Matthews correlation coefficient (MCC) and Cohen’s Kappa Statistic [49] for comparing the performance.”
MethodA real-time electrical load forecasting and unsupervised anomaly detection framework
Wang, X., Yao, Z., & Papaefthymiou, M. C. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120279 · Cited by 95
Flood susceptible prediction through the use of geospatial variables and machine learning methods
Gharakhanlou, N. M., & Pérez, L. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.129121 · Cited by 93
Synthbuster: Towards Detection of Diffusion Model Generated Images
Bammey, Q. (2023) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2023.3337714 · Cited by 125
Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93
“MCC is a more reliable metric, whereby a high score is assigned only if the prediction was correct for the four base categories (TP, FP, TN and FP), taking into account the size of P and N [85].”
BackgroundRanjbarzadeh, R., Zarbakhsh, P., Caputo, A., Tırkolaee, E. B., & Bendechache, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107723 · Cited by 94
Storylines of weather-induced crop failure events under climate change
Goulart, H. M. D., van der Wiel, K., Folberth, C., Balkovič, J., & van den Hurk, B. (2021) · Earth System Dynamics · doi:10.5194/esd-12-1503-2021 · Cited by 95
“The model requires both positive data and negative data to be correctly predicted to have a high score, which makes it particularly useful for imbalanced datasets (Chicco and Jurman, 2020).”
BackgroundMachine learning-based prediction of air quality index and air quality grade: a comparative analysis
Aram, S. A., Nketiah, E. A., Saalidong, B. M., Wang, H., Afitiri, A.-R., Akoto, A. B., & Lartey, P. O. (2023) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-023-05016-2 · Cited by 94
“The formula for the MCC based on the confusion matrix groups (true positives (TP), (9) y − ̂ y false negatives (FN), true negatives (TN), and false positives (FP)) is given as: Research by Chicco and Jurman (2020) concluded that MCC is the best accuracy measure for binary classification compared to the F1 score and Ac…”
InfluentialDiallo, R., Edalo, C., & Awe, O. O. (2024) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-031-72215-8_12 · Cited by 107
Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut
Taner, A., Öztekin, Y. B., & Duran, H. (2021) · Sustainability · doi:10.3390/su13126527 · Cited by 93
“The evaluation metrics used in the study are explained below [52].”
MethodIoT malware detection architecture using a novel channel boosted and squeezed CNN
Asam, M., Khan, S. H., Akbar, A., Bibi, S., Jamal, T., Khan, A., Ghafoor, U., & Bhutta, M. R. (2022) · Scientific Reports · doi:10.1038/s41598-022-18936-9 · Cited by 99
“It gives a high score for prediction only if all four quadrants are proportionally high for both positive and negative classes [34].”
BackgroundVilloth, J. P., Zivkovic, M. Z., Živković, T., Abdel-Salam, M., Hammad, M. A., Jovanović, L., Šimić, V., & Bačanin, N. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129695 · Cited by 97
Alghofaili, Y., & Rassam, M. A. (2022) · Sensors · doi:10.3390/s22020634 · Cited by 87
“The false positive (FP), false negative (FN), true positive (TP), and true negative (TN), as well as their relationships, are parameters often used by trust management researchers to evaluate the performance of various techniques [48].”
BackgroundZhang, C., Dong, J., & Ge, Q. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106946 · Cited by 87
Kha, Q.-H., Ho, Q., & Le, N. Q. K. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01034 · Cited by 87
“49,50 Six classifier algorithms, i.e., Random Forest (RF), Adaptive Boosting classifier (AB), Extra Tree Classifier (ET), Logistic regression (LR), Multilayer perceptron classifier (MLP), and eXtreme Gradient Boosting (XGB), were selected to make the performance comparison with our proposed model.”
MethodTransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image SegmentationPreprint
Chen, B., Liu, Y., Zhang, Z., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05274 · Cited by 87
“3) Evaluation Metrics: In our experiments, we adopt the mean Dice coefficient (DICE) [46] and F1-score [47] as the key evaluation metric to measure the extent of similarity between the predicted mask and ground truth.”
MethodAutomated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis
Ginley, B., Jen, K.-Y., Han, S. S., Rodrigues, L., Jain, S., Fogo, A. B., Zuckerman, J., Walavalkar, V., Miecznikowski, J. C., Wen, Y., et al. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020050652 · Cited by 94
Spatiotemporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction
Malawade, A. V., Yu, S.-Y., Hsu, B., Muthirayan, D. R. P., Khargonekar, P. P., & Faruque, M. A. A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3141044 · Cited by 86
Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning
Rostami, A., Shah–Hosseini, R., Asgari, S., Zarei, A., Aghdami-Nia, M., & Homayouni, S. (2022) · Remote Sensing · doi:10.3390/rs14040992 · Cited by 96
Automated accurate fire detection system using ensemble pretrained residual network
Doğan, Ş., Barua, P. D., Kutlu, H., et al. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117407 · Cited by 86
An effective intrusion detection approach based on ensemble learning for IIoT edge computing
Mohy-eddine, M., Guezzaz, A., Benkirane, S., & Azrour, M. (2022) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-022-00456-9 · Cited by 88
Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system
Du, Z., Chen, K., Chen, S., He, J., Zhu, X., & Jin, X. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113072 · Cited by 86
A Comparative Study of Class Rebalancing Methods for Security Bug Report Classification
Zheng, W., Xun, Y., Wu, X., Deng, Z., Chen, X., & Sui, Y. (2021) · IEEE Transactions on Reliability · doi:10.1109/tr.2021.3118026 · Cited by 85
Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray
Pal, D., Reddy, P. B., & Roy, S. S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106083 · Cited by 86
Fu, X., Wu, M., Tiong, R. L. K., & Zhang, L. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104672 · Cited by 89
Liu, J., & Liu, J.-C. (2021) · Geoscience Frontiers · doi:10.1016/j.gsf.2021.101311 · Cited by 88
“The multi-classification task is usually decomposed into multiple binary-classification tasks for evaluation (Chicco and Jurman, 2020).”
Extreme Precipitation on Consecutive Days Occurs More Often in a Warming Climate
Du, H., Donat, M. G., Zong, S., Alexander, L. V., Manzanas, R., Kruger, A. C., Choi, G., Salinger, J., He, H. S., Li, M., et al. (2021) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-21-0140.1 · Cited by 90
“…Matthews correlation coefficient is suited to binary data, and can effectively overcome the class imbalance issue (i.e., the number of samples in one class (e.g., “0”) is much larger than the number of samples in another class (e.g., “1”) for 0–1 binary data ( Chicco and Jurman 2020 ; Chicco et al.”
BackgroundZhou, Y., Liu, J., Chen, K., Shi, H., Shang, X., Que, Z., Shen, Z., Zhang, L., & Zeng, X. (2025) · Acta Materialia · doi:10.1016/j.actamat.2025.121322 · Cited by 87
Koçak, B., Klontzas, M. E., Stanzione, A., Meddeb, A., Demircioğlu, A., Blüthgen, C., Bressem, K. K., Ugga, L., Mercaldo, N. D., Díaz, O., et al. (2025) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2025.100030 · Cited by 119
Detecting Zero-Day Intrusion Attacks Using Semi-Supervised Machine Learning Approaches
Mbona, I., & Eloff, J. H. P. (2022) · IEEE Access · doi:10.1109/access.2022.3187116 · Cited by 85
Intelligent Deep Machine Learning Cyber Phishing URL Detection Based on BERT Features Extraction
Elsadig, M., Ibrahim, A. O., Basheer, S., Alohali, M. A., Alshunaifi, S., Alqahtani, H. M., Alharbi, N., & Nagmeldin, W. (2022) · Electronics · doi:10.3390/electronics11223647 · Cited by 85
Alaka, S. A., Menon, B. K., Brobbey, A., Williamson, T. S., Goyal, M., Demchuk, A. M., Hill, M. D., & Sajobi, T. T. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00889 · Cited by 81
“MCC values ranges between − 1 and + 1; the former represents total disagreement between observed and predicted binary classifications, while the latter represents perfect agreement (i.e., perfect prediction) (17, 18).”
BackgroundTongue image quality assessment based on a deep convolutional neural network
Jiang, T., Hu, X., Yao, X., Tu, L., Huang, J.-B., Ma, X., Cui, J. W., Wu, Q., & Xu, J. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01508-8 · Cited by 89
Basith, S., Lee, G., & Manavalan, B. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab376 · Cited by 82
“However, we compared the performances between two methods in terms of MCC, which is an intuitive and straightforward metric while dealing with an imbalanced dataset, as mentioned in [90].”
ResultMachine Learning in Network Intrusion Detection: A Cross-Dataset Generalization Study
Cantone, M., Marrocco, C., & Bria, A. (2024) · IEEE Access · doi:10.1109/access.2024.3472907 · Cited by 151
“The MCC [67], [68], [69] is a correlation coefficient between classifier output and true label, hence it yields a more informative and truthful score in evaluating binary classifications than accuracy, especially in the case of unbalanced datasets.”
BackgroundSolar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs
Díaz, J. J. V., Vlaminck, M., Lefkaditis, D., et al. (2020) · Sensors · doi:10.3390/s20216219 · Cited by 81
Çubuk, C., Garrett, A., Choi, S., King, L., Loveday, C., Torr, B., Burghel, G. J., Durkie, M., Callaway, A., Robinson, R., et al. (2021) · Genetics in Medicine · doi:10.1038/s41436-021-01265-z · Cited by 82
“We also calculated the Matthews correlation coefficient (MCC), which combines TP, TN, FP, and FN, the area under the curve (AUC), and the F1, which combines precision and recall (Supplementary Tables 7, 8) [37].”
MethodCredit Card Fraud Detection with Autoencoder and Probabilistic Random Forest
Lin, T.-H., & Jiang, J. (2021) · Mathematics · doi:10.3390/math9212683 · Cited by 82
“The RF model of AE-PRF has 100 decision trees (estimators) and uses Gini impurity as the criterion, as defined in Equation (3).”
MethodPasrija, P., Jha, P. K., Upadhyaya, P., Khan, M. S., & Chopra, M. (2022) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026622666220701091339 · Cited by 92
Yao, J., Tran, S. N., Garg, S., & Sawyer, S. (2024) · ACM Computing Surveys · doi:10.1145/3639816 · Cited by 106
“Both metrics can be calculated from a confusion matrix [13].”
BackgroundEfficient spam and phishing emails filtering based on deep learning
Magdy, S., Abouelseoud, Y., & Mikhail, M. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.108826 · Cited by 80
RBF-SVM kernel-based model for detecting DDoS attacks in SDN integrated vehicular network
Anyanwu, G. O., Nwakanma, C. I., Lee, J. M., & Kim, D. (2022) · Ad Hoc Networks · doi:10.1016/j.adhoc.2022.103026 · Cited by 79
Zoppi, T., Ceccarelli, A., Puccetti, T., & Bondavalli, A. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103107 · Cited by 92
Explainable AI-based innovative hybrid ensemble model for intrusion detection
Ahmed, U., Jiangbin, Z., Almogren, A., Khan, S., Sadiq, M. T., Altameem, A., & Rehman, A. U. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00712-x · Cited by 82
“The AUC is calculated as the area under the ROC curve [51].”
MethodLi, Q., Xu, Y. H., & Ren, C. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2993807 · Cited by 78
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making
Metsch, J. M., & Heider, D. (2021) · BioData Mining · doi:10.1186/s13040-021-00283-6 · Cited by 78
“An overview of the nine datasets ’ AUC and MCC values are shown in Tables 2 and 3, as a representative selection of metrics [33].”
An ensemble deep learning based IDS for IoT using Lambda architecture
Alghamdi, R., & Bellaïche, M. (2023) · Cybersecurity · doi:10.1186/s42400-022-00133-w · Cited by 78
“F1-score It is also known as the F-measure that considers both precision and recall (Chicco and Jurman 2020).”
BackgroundShahid, S. M., Ko, S.-H., & Kwon, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116233 · Cited by 80
An Interpretable Machine Learning Approach for Hepatitis B Diagnosis
Obaido, G., Ogbuokiri, B., Swart, T. G., Ayawei, N., Kasongo, S. M., Aruleba, K., Mienye, I. D., Aruleba, I., Chukwu, C. W., Osaye, F. J., et al. (2022) · Applied Sciences · doi:10.3390/app122111127 · Cited by 81
İban, M. C., & Bilgilioğlu, S. S. (2023) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-023-02392-6 · Cited by 78
“MCC = 0 is the predicted value for the coin tossing classifier, and it lies between the range [1, + 1], with extreme values − 1 and + 1 achieved in the event of perfect misclassification and perfect classification, respectively (Chicco and Jurman 2020 ).”
BackgroundMao, Y.-J., Lim, H.-J., Ni, M., Yan, W.-H., Wong, D. W., & Cheung, J. C. (2022) · Cancers · doi:10.3390/cancers14020367 · Cited by 80
“However, the measure fails to account for the ratio between positive and negative tests and is thus not recommended to be used for highly imbalanced class problems that commonly appear in health science [77].”
BackgroundEarthquake risk assessment in NE India using deep learning and geospatial analysis
Jena, R., Pradhan, B., Naik, S. P., et al. (2021) · Geoscience Frontiers · doi:10.1016/j.gsf.2020.11.007 · Cited by 75
“The classifier’s performance can be presented as accuracy (Chicco and Jurman, 2020 ) : From the harmonic mean of precision and recall , F-1 score can be computed as (Chicco and Jurman, 2020 ) : F-1= ( According to the obtained result, the achieved accuracy was 94%.”
MethodSingh, V. K., & Kolekar, M. H. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11158-7 · Cited by 77
Taherkhani, K., Sheydaeian, E., Eischer, C., et al. (2021) · Additive manufacturing · doi:10.1016/j.addma.2021.102152 · Cited by 75
Hybrid data-driven and model-informed online tool wear detection in milling machines
Yang, Q., Pattipati, K. R., Awasthi, U., & Bollas, G. M. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.04.001 · Cited by 75
Amin, A., Adnan, A., & Anwar, S. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110103 · Cited by 83
Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method
Yıldırım, M., & Çınar, A. (2020) · Ingénierie des systèmes d information · doi:10.18280/isi.250402 · Cited by 75
“True Negative (TN): What is really negative is that the data is estimated negatively [17].”
BackgroundSingh, G., Reynolds, C., Byrne, M. J., & Rosman, B. (2020) · Remote Sensing · doi:10.3390/rs12244021 · Cited by 85
“However, for binary classification, especially with imbalanced data cases, the MCC metric is favored over other accuracy metrics, including the overall accuracy and F1-score metric [58] because the MCC metric is high when the majority of both the positive and negative instances have been successfully discriminated.”
BackgroundSeverino, M. K., & Peng, Y. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100074 · Cited by 74
Radhakrishnan, T., Karhade, J., Ghosh, S. K., Muduli, P. R., Tripathy, R. K., & Acharya, U. R. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104783 · Cited by 75
Hao, J., Chen, P., Chen, J., & Li, X. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.006 · Cited by 74
Prediction of fall risk among community-dwelling older adults using a wearable system
Lockhart, T. E., Soangra, R., Yoon, H., Wu, T., Frames, C. W., Weaver, R. H., & Roberto, K. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-00458-5 · Cited by 77
Muslim, M. A., Nikmah, T. L., Pertiwi, D. A. A., et al. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200204 · Cited by 73
Alencastre-Miranda, M., Johnson, R. M., & Krebs, H. I. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2992229 · Cited by 76
“MCC is a statistical score typically employed for the binary classifications of unbalanced data sets (our case) [31].”
MethodMusulin, J., Štifanić, D., Zulijani, A., Ćabov, T., Dekanić, A., & Car, Z. (2021) · Cancers · doi:10.3390/cancers13081784 · Cited by 81
“It is an overall measure of a model’s accuracy and can be defined as [54]:”
BackgroundMedical-informed machine learning: integrating prior knowledge into medical decision systems
Sirocchi, C., Bogliolo, A., & Montagna, S. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02582-4 · Cited by 89
“Performance was evaluated using accuracy (A), F1-score (F1), recall (R), precision (P), balanced accuracy (BA), area under the curve of the Receiver Operating Characteristic curve (ROC) and Matthews correlation coefficient (MCC) [66].”
MethodIntegrative Network Fusion: A Multi-Omics Approach in Molecular Profiling
Chierici, M., Bussola, N., Marcolini, A., Francescatto, M., Zandonà, A., Trastulla, L., Agostinelli, C., Jurman, G., & Furlanello, C. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01065 · Cited by 82
“The model performance is assessed in terms of average Matthews Correlation Coefficient (MCC) (17, 18), which is generally regarded as a balanced measure of accuracy and precision that can be used both in binary bioRxiv 6 and multiclass problems (19, 20) and even when classes are imbalanced (21).”
MethodSelf-citation
Metadata
- Authors
- Chicco, D., & Jurman, G.
- Affiliations
- Krembil Research Institute (CA)
- Fondazione Bruno Kessler (IT)
- Corresponding author
- Chicco, Davide
- Venue
- BMC Genomics
- Published
- 2020-01-02
- Volume
- 21
- Issue
- 1
- Pages
- 6-6
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC6941312
- OpenAlex
- W2999309192
- Semantic Scholar
- 44858b662822d291a5e1e9e3501ad19a9f1572c2
- Cited by
- 6,143
- References
- 104
- Citations by year
- 2026: 1118 · 2025: 1152 · 2024: 1060 · 2023: 1032 · 2022: 883 · 2021: 682 · 2020: 211
- FWCI
- 276.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Iris (University of Trento) (submittedVersion)
- Funders
- University of Toronto
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Matthews correlation coefficient, F1 score, accuracy, confusion matrix, class imbalance, classification metrics
- MeSH terms
- Machine Learning, Correlation of Data, Algorithms, Data Interpretation, Statistical, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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