The DOI matches its Crossref record; 41 of the 43 references it cites were found in a registry or an index.
A Comparison of MCC and CEN Error Measures in Multi-Class Prediction
Open accessAbstract
We show that the Confusion Entropy, a measure of performance in multiclass problems has a strong (monotone) relation with the multiclass generalization of a classical metric, the Matthews Correlation Coefficient. Analytical results are provided for the limit cases of general no-information (n-face dice rolling) of the binary classification. Computational evidence supports the claim in the general case.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In the citing paper
“MCC is the only binary classification rate that generates a high score only if the binary predictor was able to correctly predict the majority of positive data instances and the majority of negative data instances [80, 97].”
From The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020) · cites this paper as [80]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
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
The Proceedings of the National Academy of Sciences
Richards, J. W. (1915) · Science · doi:10.1126/science.42.1074.161 · Cited by 13,941
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,051
A systematic analysis of performance measures for classification tasks
Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
Repeated observation of breast tumor subtypes in independent gene expression data sets
Sørlie, T., Tibshirani, R. J., Parker, J. S., Hastie, T. J., Marron, J. S., Nobel, A. B., Deng, S., Johnsen, H., Pesich, R., Geisler, S. B., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0932692100 · Cited by 5,508
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
Matthews, B. W. (1975) · Biochimica et Biophysica Acta (BBA) - Protein Structure · doi:10.1016/0005-2795(75)90109-9 · Cited by 5,399 · cited 1×
“This latter function was introduced in (Matthews, 1975) and it is also known as the φ -coe ffi cient, corresponding for a 2 × 2 contingency table to the square root of the average χ 2 statistic χ 2 / n . p MCC has recently attracted the attention of the machine learning community (Baldi et al., 2000)…”
Background
Flagged first, then by citations
2 of the 43 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [11]Landgrebe T, Duin R (2005) On Neyman-Pearson optimisation for multiclass classifiers. In: Proceedings 16th Annual Symposium of the Pattern Recognition Association of South Africa. PRASA, pp. 165–170.
- [12]Landgrebe T, Duin R (2006) A simplified extension of the Area under the ROC to the multiclass domain. In: Proceedings 17th Annual Symposium of the Pattern Recognition Association of South Africa. PRASA, pp. 241–245.
Cited by
Sorted by citations
Items 301–400 of 404
Developing analytics models for software project management
Choetkiertikul, M. (2018) · Research Online (University of Wollongong) · Cited by 3
From partial to whole genome imputation of SARS-CoV-2 for epidemiological surveillancePreprint
Ortuño, F. M., Loucera, C., Casimiro‐Soriguer, C. S., Lepe, J. A., Camacho‐Martínez, P., Díaz, L. M., de Salazar, A., Chueca, N., García, F., Pérez-Florido, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.13.439668 · Cited by 3
A Transformers-based framework for refinement of genetic variants
Abdelwahab, O., & Torkamaneh, D. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1694924 · Cited by 2
Chicco, D., Melzi, S., Gasparini, F., & Jurman, G. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3424 · Cited by 2
Self-citationWang, Q., & Xu, Y. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9903-8 · Cited by 2
Using Machine Learning for Inter-smell Detection: A Feasibility Study
Gupta, R., & Singh, S. K. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-21385-4_25 · Cited by 2
Tracking customer risk aversion
Kong, H., Yun, W., & Kim, W. C. (2023) · Finance research letters · doi:10.1016/j.frl.2023.103698 · Cited by 2
Formability classifier for a TV back panel part with machine learning
Fazily, P., Cho, D., Choi, H., Cho, J. H., Lee, J.-S., & Yoon, J. W. (2023) · International Journal of Material Forming · doi:10.1007/s12289-023-01791-y · Cited by 2
Chen, Y.-S., Kao, C.-L. M., Liu, P.-H., & Tseng, V. S. M. (2024) · Computational Economics · doi:10.1007/s10614-024-10673-7 · Cited by 4
A JADE based testbed for evaluating computational trust models
Youssef, M., En‐Nouaary, A., & Dahchour, M. (2015) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1109/sita.2015.7358407 · Cited by 2
PAFS - An efficient method for classifier-specific feature selection
Huy, P. Q., Ngom, A., & Rueda, L. G. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7850131 · Cited by 2
Institut National des Postes et Télécommunications, R., Youssef, M., En‐Nouaary, A., & Dahchour, M. (2017) · Journal of Software · doi:10.17706/jsw.12.11.892-905 · Cited by 2
A Multi-objective Meta-Analytic Method for Customer Churn Prediction
Haque, M. N., de Vries, N. J., & Moscato, P. (2019) · Springer eBooks · doi:10.1007/978-3-030-06222-4_20 · Cited by 2
Azam, M., & Bouguila, N. (2019) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2019.00034 · Cited by 2
Applicability of Deep Neural Networks on the Task of Document Retrieval
Malik, M. S., & Waltemath, D. (2020) · doi:10.5121/csit.2020.101123 · Cited by 2
Assessing Capsule Networks with Biased Data
Ferrarini, B., Ehsan, S., Bartoli, A., Leonardis, A., & McDonald-Maier, K. D. (2019) · Open Access at Essex (University of Essex) · Cited by 2
Machine intelligence aware electricity theft detection for smart metering applications
Munawar, S., Khan, Z. A., Chaudhary, N. I., Javaid, N., & Raja, M. A. Z. (2023) · Waves in Random and Complex Media · doi:10.1080/17455030.2023.2239368 · Cited by 2
Husejinovic, A., Kevrić, J., Durmić, N., & Jukić, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-43056-5_19 · Cited by 2
Classification of Cybercrime Indicators in Open Social Data
Ullah, I., Lane, C., Buda, T. S., Drury, B., Mellotte, M., Assem, H., & Madden, M. G. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-76228-5_23 · Cited by 2
Machine Learning methods for mood disorder decision support
Oleksy, T. (2017) · NORA - Norwegian Open Research Archives · Cited by 2
Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images
Tampu, I. E., Eklund, A., & Haj-Hosseini, N. (2022) · Scientific Data · doi:10.1038/s41597-022-01618-6 · Cited by 1
13. DISTINGUISHING DIFFERENT PSYCHIATRIC DISORDERS USING DDX-PRS
Peyrot, W. J., Panagiotaropoulou, G., Loohuis, L. M. O., Adams, M. J., Andreassen, O. A., Awasthi, S., Forstner, A. J., Ge, T., McIntosh, A. M., Mitchell, B. L., et al. (2022) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2022.07.104 · Cited by 1
Huang, Y., Krishnan, G. S., O’Connor, T., Joshi, R. C., & Javidi, B. (2024) · SPIE Three-Dimensional Imaging, Visualization, and Display · doi:10.1117/12.3013527 · Cited by 1
Maestro, P., Romero, P. E., Ramírez‐Sanz, J. M., & Bustillo, A. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02704-3 · Cited by 1
Aguilar–Ruiz, J. S. (2025) · Mathematics · doi:10.3390/math13243956 · Cited by 1
Enzyformer: a two-stage pretrained model for enzymatic retrosynthesis
Liu, T., Xu, J., Zhan, X., Lin, S., & Siu, S. W. I. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01164-y · Cited by 4
Crowder, C. J., Yik, B. J., Frost, S. J. H., de Arellano, D. C., Bliss-Roche, K., & Raker, J. R. (2026) · Journal of Science Education and Technology · doi:10.1007/s10956-026-10304-w · Cited by 1
Machine-Learning-Based GNSS Signal Anomaly Classification Using COSMIC-2 POD Data
Liang, Z., Jin, R., Cui, X., Chen, L., Zhen, W., Peng, H., Yang, H., Gu, M., & Ji, G. (2026) · Remote Sensing · doi:10.3390/rs18183074 · Cited by 1
Mifrah, Y., En‐Nouaary, A., & Dahchour, M. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-1627-1_45 · Cited by 1
A new metric to assess the predictive accuracy of multinomial land cover models
Douma, J. C., Cornwell, W. K., & van Bodegom, P. M. (2017) · Journal of Biogeography · doi:10.1111/jbi.12983 · Cited by 1
Primary tumor type prediction based on US nationwide genomic profiling data in 13,522 patients
Huang, Y., Pfeiffer, S. M., & Zhang, Q. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.07.036 · Cited by 1
Schaffer, M., Hansen, A. R., Vera‐Valdés, J. E., & Marszal-Pomianowska, A. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2600/5/052004 · Cited by 1
Sirico, A., & Herber, D. R. (2024) · AIAA SciTech Forum · doi:10.2514/6.2024-0684 · Cited by 3
Hierarchical mixture of discriminative Generalized Dirichlet classifiers
Togban, E., & Ziou, D. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110789 · Cited by 1
A Hybrid Model for Detection and Classification of Fishing Activity: A Context-Based Approach
Rangel, P., Nunes, V. M. D. A., Salazar, M. D. R., Simões, R. A. G., Rosa, L. M. D. C., & de Carvalho, J. G. (2024) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion59988.2024.10706303 · Cited by 1
Using Electronic Health Records to Identify the Daily Primary Provider During Hospitalization
Ingraham, N. E., Shyu, D., Phelan, T., MESFIN, N. M., Langworthy, B., Kohn, R. A., Kerlin, M. P., & Dudley, R. A. (2024) · Critical Care Explorations · doi:10.1097/cce.0000000000001189 · Cited by 1
Anđelić, N., Šegota, S. B., & Mrzljak, V. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9020027 · Cited by 1
Addressing the Problem of Lysine Glycation Prediction in Proteins via Recurrent Neural Networks
Que-Salinas, U., Martinez-Peón, D., Méndez, G. M., Argüelles-Lucho, P., Reyes-Figueroa, A. D., & Scheckhuber, C. Q. (2025) · BioMed Research International · doi:10.1155/bmri/2426944 · Cited by 1
Developing a framework for promoting physical activity in a Boccia game scenario
da Silva, V. M. S., Ramos, J., Leite, P. A. S., Soares, F., Nováis, P., Arezes, P., Sousa, F., Figueira, C., & Santos, A. L. (2018) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2018.1538816 · Cited by 1
Sen, R., Tagore, S., & De, R. K. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103374 · Cited by 1
Information leakage prevention based on the separation of encrypted and compressed data
Kozachok, A. V., Spirin, A. A., Kozachok, V., & Lavrova, D. S. (2021) · Ivannikov ISPRAS Open Conference (ISPRAS) · doi:10.1109/ispras53967.2021.00009 · Cited by 1
Not again! Data Leakage in Digital PathologyPreprint
Bussola, N., Marcolini, A., Maggio, V., Jurman, G., & Furlanello, C. (2019) · arXiv (Cornell University) · Cited by 1
Self-citationde Lima, R. R., Fernandes, A. M. D. R., Bombasar, J. R., da Silva, B. A., Crocker, P. A., & Leithardt, V. R. Q. (2021) · Preprints.org · doi:10.20944/preprints202111.0378.v1 · Cited by 1
Zhang, X., Akber, M. Z., & Zheng, W. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4020707 · Cited by 1
Socio-endocrinology revisited: New tools to tackle old questions
Christensen, C. (2022) · Cronfa (Swansea University) · doi:10.23889/suthesis.59827 · Cited by 1
Tolossa, G. B., Schneider, A. M., Dyer, E. L., & Hengen, K. B. (2024) · eLife · doi:10.7554/elife.101506.1 · Cited by 1
Tolossa, G. B., Schneider, A. M., Dyer, E. L., & Hengen, K. B. (2025) · eLife · doi:10.7554/elife.101506.2 · Cited by 1
Protein Secondary Structure Prediction Using a Vector Valued Classifier
Korff, M. G. (2016) · Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · doi:10.17169/refubium-8667
Taylor, D. W. (2017) · Surrey Research Insight Open Access (The University of Surrey)
Thibodeau, A. (2018) · OpenCommons at University of Connecticut (University of Connecticut)
Neuromechanical and Environment Aware Machine Learning Tool for Human Locomotion Intent Recognition
Carvalho, S. P., Figueiredo, J., & Santos, C. P. (2019) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-31635-8_240
Seeling, P., & Fitzek, F. H. P. (2020) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc46108.2020.9045212
Desenvolvimento e Análise de Algoritmos Aplicados na Predição de Acidentes em Ambiente Fabril
Ksenhuck, B. C., Vieira, M. E. M., Lechuga, T. A., Bezerra, F. O., & Corrêa, P. L. P. (2019) · Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware) · doi:10.5753/latinoware.2019.10329
Multi-Omic Predictors of Steatohepatitis and Advanced Fibrosis in ChildrenPreprint
Kordy, K., Li, F., Lee, D. J., Kinchen, J. M., Jew, M., Rocque, M. E. L., Martínez, S., Zabih, S., Saavedra, M., Woodward, C. L., et al. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3299431
Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft
ALDabbas, A., Gál, Z., Ghori, K. M., Imran, M. A., & Shoaib, M. (2021) · IEEE Access · doi:10.1109/access.2021.3064753
Predictive QSAR tools to aid in early process development of monoclonal antibodies
Karlberg, J. M. A. (2019)
On Data Windows for Fault Detection with Neural Networks
Šprogar, M., Colnarič, M., & Verber, D. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.10.007
Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets
Bizzego, A., Gabrieli, G., Neoh, M. J. Y., & Esposito, G. (2021) · Bioengineering · doi:10.3390/bioengineering8120193 · Cited by 4
Ecomorphological Variation in Trithemis (Odonata, Libellulidae) Dragonfly Wings ReconsideredPreprint
MacLeod, N., Price, B. W., & Stevens, Z. (2021) · Research Square · doi:10.21203/rs.3.rs-962784/v1
Novidianto, R., Fithriasari, K., & Kuswanto, H. (2023) · AIP conference proceedings · doi:10.1063/5.0107155
Parametric PDF for Goodness of Fit
Katz, N., & Utai, U. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1147
Clasificación automática de correos electrónicos
Fernández, J. M. (2023) · doi:10.35537/10915/152375
Hierarchical Evolutionary Ensemble of Multiple ClassifiersPreprint
Zhen, X., Liang, F., Mo, T., Wang, L., & Tang, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4495110
Hierarchical Evolutionary Ensemble of Multiple ClassifiersPreprint
Liang, F., Mo, T., Wang, L., Tang, P., & Zhen, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4601010
Dalvi-Garcia, F., Quagliato, L. A., Bearden, D. J., & Nardi, A. E. (2023) · Brazilian Journal of Psychiatry · doi:10.47626/1516-4446-2023-3291
Toward Solving Domain Adaptation with Limited Source Labeled Data
Chen, K., Rognvaldsson, T. S., Nowaczyk, S., Pashami, S., Klang, J., & Sternelöv, G. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00161
Clustering Social Media Data for Bitcoin Price Prediction with Transformer Model
Zhi, Y., & Chan, T.-H. H. (2023) · International Conference on Machine Learning and Natural Language Processing (MLNLP) · doi:10.1145/3639479.3639508 · Cited by 1
A Comprehensive Review on Detection of Dacrocytes from Blood Smear Images using ML Algorithms
Kavitha, D., Padmavathi, B., Shakthi Priya, S., Sanjana, N., & Sidesh, S. (2023) · International Conference on Recent Trends in Advance Computing (ICRTAC) · doi:10.1109/icrtac59277.2023.10480795
Gómez, A. B. (2024) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/205742
Transfer learning based tunnel boring machine advance classification
Unterlaß, P. J., Erharter, G. H., & Marcher, T. (2024) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1435/1/012008
An Effective Deep Learning Approach for Fault Detection and Classification of Levitation Processes
Képešiová, Z., & Kozák, Š. (2025) · Cybernetics & Informatics (K&I) · doi:10.1109/ki64036.2025.10916461
Kirchgaessner, R., Keutler, K., Sivakumar, L., Song, X. B., & Ellrott, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.14.653900
Interpretable chip-quality classification with signal and feature selection in wire bonding
Rosman, C., Schelthoff, K., & Akçay, A. (2025) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-025-09621-w
Integrating predictive modeling with sentiment analysis for Lassa fever prediction
Akhetuamen, S. O., Konyeha, S., & Ukhurebor, K. E. (2025) · Next research. · doi:10.1016/j.nexres.2025.100586
Wang, D., Shang, L., & Zhang, Y. (2025) · Springer eBooks · doi:10.1007/978-3-031-90080-8_7
Goyal, B., Hans, R., & Sharma, S. K. (2025) · International Conference on Electronics, AI and Computing (EAIC) · doi:10.1109/eaic66483.2025.11101390
Das, N., & Chakraborty, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111023
DispaRisk: Assessing Fairness Through Usable Information
Vasquez, J., Domeniconi, C., & Rangwala, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05962-8_18
Chicco, D., & Jurman, G. (2023) · Figshare · doi:10.6084/m9.figshare.22606970
Self-citationChicco, D., Tötsch, N., & Jurman, G. (2021) · Figshare · doi:10.6084/m9.figshare.13719529
Self-citationKiadaliri, A., Lohmander, L. S., Ignjatovic, M. M., Nero, H., & Dahlberg, L. (2023) · Figshare · doi:10.6084/m9.figshare.22621289
Rupa, S. A., Arifin, K. N., & Anwar, M. M. (2026) · Engineering Reports · doi:10.1002/eng2.70576
DDSS: Detecting Different Stuttered Speech Using Various Feature Extraction Techniques
Batra, A., Hema, Y., Rao, V., & Das, P. K. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-14534-5_26
Computer vision-based crop loss assessment for smallholder agriculturePreprint
Achicanoy, H., Ramírez-Villegas, J., Meuwissen, M., Kramer, B., Ceballos, F., Hufkens, K., & Dalhaus, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6176863
Tool-Wear Classification Using Sound-Based Ensemble Learning in the Turning of AISI 1050 Steels
Koç, S., & İlenç, R. (2026) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-026-13434-9
Real-Time Web-Based BISINDO Alphabet Recognition Using FastAPI and WebSockets
Chiari, I., Husaini, Saputra, K., Zahnur, & Misbullah, A. (2025) · International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE) · doi:10.1109/cosite68330.2025.11414264
Lihua, F. U., & ZHANG, L. (2026) · Progress in Disaster Science · doi:10.1016/j.pdisas.2026.100616
Pooling multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal
Kirchgaessner, R., Keutler, K., Sivakumar, S., Song, X., & Ellrott, K. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag159
Real-time intrusion detection in IoT networks: A hybrid framework of offline ML and online DL
Kilichev, D., & Kim, W. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102039
Metadata
- Authors
- Jurman, G., Riccadonna, S., & Furlanello, C.
- Affiliations
- Fondazione Bruno Kessler (IT)
- Corresponding author
- Jurman, Giuseppe
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-08-08
- Volume
- 7
- Issue
- 8
- Pages
- e41882
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3414515
- OpenAlex
- W1988301066
- Cited by
- 406
- References
- 43
- Citations by year
- 2026: 17 · 2025: 29 · 2024: 38 · 2023: 54 · 2022: 47 · 2021: 54 · 2020: 32 · 2019: 36 · 2018: 25 · 2017: 23 · 2016: 15 · 2015: 12 · 2014: 14 · 2013: 10
- FWCI
- 11.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- Matthews correlation coefficient, multi-class classification, generalization, binary classification, monotonicity
- MeSH terms
- Algorithms, Artificial Intelligence, Computers, Genotype, Probability, ROC Curve, Software, Statistics as Topic, Reproducibility of Results, Models, Statistical, Entropy, Computational Biology, Area Under Curve, Oligonucleotide Array Sequence Analysis
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review