Learning Pattern Classification Tasks with Imbalanced Data Sets
Open accessAbstract
&RQFHSW FRPSOH[
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
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?
SMOTE has significant drawbacks when the minority classes have a limited number of samples.
“as SMOTE has significant drawbacks when the minority classes have a limited number of samples [58].”
From Detecting Developers' Task Switches and Types (2022) · cites this paper as [58]
Section 3.3 (Advanced sampling methods; page not provided) says that inadequate minority-class examples can make distribution estimates inaccurate, but it identifies this as a drawback of generative over-sampling, a variation of SMOTE. It does not establish that SMOTE itself has this drawback. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 35
- Alejo, R., García, V., Sotoca, J., Mollineda, R. & Sánchez, J. (2007). Improving the Performance of the RBF Neural Networks Trained with Imbalanced Samples, In Proceedings of Computational and Ambient Intelligence, 9th International Work-Conference on Artificial Neural Networks, pp. 162-169, San Sebastian, Spain, 2007.
- Caruanan, R. (2000). Learning from Imbalanced Data: Rank Metrics and Extra Tasks, The AAAI Workshop on Learning from Imbalanced Data Sets, pp. 51-57, 2000.
- Chong, E. K. P. & Zak, S. H. (1996) An Introduction to Optimization, New York, John Wiley and Sons, Inc.
- Eavis, T. & Japkowicz, N. (2000). A Recognition-Based Alternative to Discrimination-Based Multi-layer Perceptrons, The 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence, pp. 280-292, London, UK, 2000, Springer-Verlag.
- Elkan, C. (2001). The Foundations of Cost-sensitive Learning, In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pp. 73-978, 2001.
- Ertekin, S., Huang, J., Bottou, L. & Giles, C. L. (2007). Learning on the Border: Active Learning in Imbalanced Data Classification, In Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 127-136, Lisbon, Portugal, 2007, ACM Press.
- Fawcett, T. (2006) An Introduction to ROC Analysis. Pattern Recognition Letters, 27, 861-874.
- Guo, H. & Viktor, H. L. (2004) Learning from Imbalanced Data Dets with Boosting and Data Generation: the DataBoost-IM Approach. ACM SIGKDD Explorations Newsletter, 6, 30-39.
- Hagan, M. T. & Menhaj, M. B. (1994) Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks, 5, 989-993.
- Hido, S. & Kashima, H. (2008). Roughly Balanced Bagging for Imbalanced Data., In Proceedings of the SIAM International Conference on Data Mining, pp. 143-152, Atlanta, Georgia, USA, 2008.
- Holte, R., Acker, L. & Porter, B. (1989). Concept Learning and the Problem of Small Disjuncts, In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp., Austin, TX, USA, 1989, Morgan Kaufmann.
- Japkowicz, N. (2001). Concept-Learning in the Presence of Between-Class and Within-Class Imbalances, In Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence, pp., London, UK, 2001, Springer-Verlag.
- Japkowicz, N., Mayers, C. & Gluck, M. (1995). A Novelty Detection Approach to Classification In Proceedings of the Fourteenth Joint Conference on Artificial Intelligence, pp. 518-523, 1995.
- Karagiannopoulos, M. G., Anyfantis, D. S., Kotsiantis, S. B. & Pintelas, P. E. (2007). Local Cost Sensitive Learning for Handling Imbalanced Data Sets, In Proceedings of Mediterranean Conference on Control and Automation, pp. 1-6, 2007.
- Kubat, M. & Matwin, S. (1997). Addressing the Curse of Imbalanced Training Set: One-sides Selection, In Proceedings of the Fourteenth International Conference on Machine Learning, pp. 179-186, 1997, Morgan Kaufmann.
- Landgrebe, T., Pacli'k, P., Tax, D. J. M., Verzakov, S. & Duin, R. P. W. (2004). Cost-based Classifier Evaluation for Imbalanced Problems, In Proceedings of The 10th International Workshop on Structural and Syntactic Pattern Recognition and the 5th International Workshop on Statistical Techniques in Pattern Recognition, pp. 762-770, Lisbon, Portugal, 2004, Springer Verlag, Berlin.
- Leskovec, J. & Shawe-Taylor, J. (2003). Linear Programming Boosting for Uneven Datasets, In Proceedings of The twentieth International Conference on Machine Learning pp. 456-463, 2003, AAI Press.
- Ling, C. X. & Li, C. (2004). Decision Trees with Minimal Costs, In Proceedings of the Twenty-first International Conference on Machine Learning, pp. 69, Banff, Alberta, Canada, 2004, ACM.
- Liu, A., Ghosh, J. & Martin, C. (2007). Generative Oversampling for Mining Imbalanced Datasets, In Proceedings of The 2007 International Conference on Data Mining, pp., Las Vegas, Nevada, USA, 2007, CSREA Press.
- Liu, Y. & Shriberg, E. (2007). Comparing Evaluation Metrics for Sentence Boundary Detection, In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 185-188, 2007.
- Murphey, Y. L., Wang, H., Ou, G. & Feldkamp, L. (2007). OAHO: and Effective Algorithm for Multi-class Learning from Imbalanced Data, Proceedings of the International Joint Conference on Neural Networks, pp. 406-411, Orlando, Florida, USA, 2007, IEEE.
- Nguyen, C. & Ho, T. (2005). An Imbalanced Data Rule Learner, In Proceedings of The 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 617-624, Porto, Portugal, 2005.
- Nguyen, G. H., Bouzerdoum, A. & Phung, S. L. (2008). A Supervised Learning Approach for Imbalanced Data Sets, In Proceeding of International Conference on Pattern Recognition, pp. 1-4, Florida, USA, 2008.
- Raskutti, B. & Kowalczyk, A. (2004) Extreme Re-balancing for SVMs: a Case Study. ACM Sigkdd Explorations Newsletter, 6, 60-69.
- Riedmiller, M. & Braun, H. (1993). A Direct Adaptive Method of Faster Backpropagation Learning: The RPROP Algorithm, IEEE International Conference on Neural Networks, pp. 586-591, San Francisco, 1993.
- Su, C. & Hsiao, Y. (2007) An Evaluation of Robustness of MTS for Imbalanced Data. IEEE Transaction on Knowledge and Data Engineering, 19, 1321-1332.
- Tao, Q., Gao Wei Wu, Fei Yue Wang & Wang, J. (2005) Posterior Probability Support Vector Machines for Unbalanced Data. IEEE Transaction on Neural Networks, 16, 1561-1573.
- Visa, S. & Ralescu, A. (2005). The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study, In Proceedings of the IEEE Conference on Fuzzy Systems, pp. 749-754, 2005.
- Wang, S. & Yao, X. (2009). Diversity Analysis on Imbalanced Data Sets by Using Ensemble Models, In Proceedings of The IEEE Symposium on Computational Intelligence and Data Mining, pp. 324-331, 2009, IEEE.
- Weiss, G. M. (2004) Mining with Rarity: a Unifying Framework. SIGKDD Explorations and Newsletters, 6, 7-19.
- Weiss, G. M. & Provost, F. (2003) Learning When Training Data Are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence Research 19, 315-354.
- Weng, C. G. & Poon, J. (2008). A New Evaluation Measure for Imbalanced Datasets, In Proceedings of The Seventh Australasian Data Mining Conference pp. 27-32, Glenelg, South Australia, 2008, ACS.
- Yoon, K. & Kwek, S. (2005). An Unsupervised Learning Approach to Resolving the Data Imbalanced Issue in Supervised Learning Problems in Functional Genomics, In Proceedings of The Fifth International Conference on Hybrid Intelligent Systems, pp. 303-308, Washington, DC, USA, 2005, IEEE Computer Society.
- Yu, T., Jan, T., Simoff, S. & Debenham, J. (2007) A Hierarchical VQSVM for Imbalanced Data Sets. In Proceedings of The International Joint Conference on Neural Networks.
- Zhou, Z.-H. & Liu, X.-Y. (2006) Training Cost-sensitive Neural Networks with Methods Addressing The Class Imbalance Problem. IEEE Transaction on Knowledge and Data Engineering, 18, 63-77.
Cited by
Sorted by citations
One-class classification: taxonomy of study and review of techniques
Khan, S. S., & Madden, M. G. (2014) · The Knowledge Engineering Review · doi:10.1017/s026988891300043x · Cited by 605 · see citing works
“In Section 4, we propose a taxonomy for the study of OCC algorithms and present a comprehensive review of the current state of the art and significant research contributions under the branches of the proposed taxonomy.”
InfluentialSoftware defect prediction using ensemble learning on selected features
Laradji, I., Alshayeb, M., & Ghouti, L. (2014) · Information and Software Technology · doi:10.1016/j.infsof.2014.07.005 · Cited by 384 · see citing works
“The geometric mean (G-mean) measure yields more accurate classification measures when the underlying data is impacted with imbalance [46].”
BackgroundMethodInfluentialClassification with class imbalance problem: A review
Ali, A., Shamsuddin, S. M., & Ralescu, A. (2015) · Cited by 324 · see citing works
Elderly activities recognition and classification for applications in assisted living
Chernbumroong, S., Cang, S., Atkins, A. S., & Yu, H. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.09.004 · Cited by 317 · see citing works
DeepJIT: An End-to-End Deep Learning Framework for Just-in-Time Defect Prediction
Hoang, T., Dam, H. K., Kamei, Y., Lo, D., & Ubayashi, N. (2019) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2019.00016 · Cited by 248 · see citing works
“, precision, recall, or F1) since these measures strongly depend on arbitraily thresholds [59], [60].”
BackgroundFeature Selection for Interpatient Supervised Heart Beat Classification
Doquire, G., de Lannoy, G., François, D., & Verleysen, M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/643816 · Cited by 193 · see citing works
“Spurious features can harm the classifier, especially in the presence of unbalanced classes and a large number of features [2, 3].”
BackgroundA Review on Handling Imbalanced Data
Vimalraj, S. S., & Porkodi, R. (2018) · International Conference on Current Trends towards Converging Technologies (ICCTCT) · doi:10.1109/icctct.2018.8551020 · Cited by 200 · see citing works
Data preprocessing for heart disease classification: A systematic literature review
Benhar, H., Idri, A., & Fernández‐Alemán, J. L. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105635 · Cited by 162 · see citing works
Tasci, E., Zhuge, Y., Camphausen, K., & Krauze, A. V. (2022) · Cancers · doi:10.3390/cancers14122897 · Cited by 117 · see citing works
“However, obtaining balanced data in medical data sets of all subtypes is challenging secondary to limitations in patient numbers, cost, privacy a d security of data sharing, and the complexity of generat d data, resulting in significant efforts to achi ve representative data sets [10,11].”
BackgroundInfluentialVariance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data
Ebenuwa, S. H., Sharif, M. S., Alazab, M., & Al-Nemrat, A. (2019) · IEEE Access · doi:10.1109/access.2019.2899578 · Cited by 114 · see citing works
“External effects have more influence on the imbalanced data; missing data or general noise has more impact on a data distribution that is imbalanced than those that are more closely balanced; the more a dataset is imbalanced, the greater the impact of noise on the model built using the data [5].”
BackgroundDeep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings
Sampedro, C., Rodríguez-Vázquez, J., Rodríguez-Ramos, A., Carrio, A., & Campoy, P. (2019) · IEEE Access · doi:10.1109/access.2019.2931144 · Cited by 115 · see citing works
“doubles the number of the positive ones, in this section we consider several metrics, computed using scikit-learn, that are usually utilized in the state-of-the-art for imbalanced datasets [51]:”
MethodLeyk, S., Uhl, J. H., Balk, D. L., et al. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.08.035 · Cited by 113 · see citing works
“1 Confusion matrices for computing accuracy measures—The GHSL raster and the created reference surfaces are compared pixel-wise to build confusion matrices, which allow the derivation of various accuracy measures (Fielding and Bell, 1997; Nguyen et al., 2009) for different time periods and each of the study areas.”
MethodImputation of missing data with class imbalance using conditional generative adversarial networks
Awan, S. E., Bennamoun, M., Sohel, F. A., Sanfilippo, F. M., & Dwivedi, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.010 · Cited by 106 · see citing works
“Since most of the machine learning models are designed with the assumption of equal number of samples of each class, they over-classify the majority class and ignore the minority class [17].”
BackgroundThamrin, S. A., Arsyad, D. S., Kuswanto, H., Lawi, A., & Nasir, S. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.669155 · Cited by 100 · see citing works
“A matrix, known as the confusion matrix, is used to evaluate the learning algorithm (37).”
MethodOn the relative value of data resampling approaches for software defect prediction
Bennin, K. E., Keung, J., & Monden, A. (2018) · Empirical Software Engineering · doi:10.1007/s10664-018-9633-6 · Cited by 97 · see citing works
“The objective functions of most machine learning algorithms assume that datasets are balanced prior to training (Chawla et al. 2002; Phung et al. 2009; Laradji et al. 2015).”
What variables are important in predicting bovine viral diarrhea virus? A random forest approach
Machado, G., Recamonde‐Mendoza, M., & Corbellini, L. G. (2015) · Veterinary Research · doi:10.1186/s13567-015-0219-7 · Cited by 97 · see citing works
“Given the severe class imbalance observed in the data and the general difficulty of machine learning methods to handle this issue [37], we have incorporated a downsampling procedure in the model learning functions provided by the caret R package, which samples the majority class to make its frequency closer to the rar…”
MethodFinding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes
Zhou, J., Pacheco, M., Wan, Z., Xia, X., Lo, D., Wang, Y., & Hassan, A. E. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678720 · Cited by 97 · see citing works
Dealing with missing values in proteomics data
Kong, W., Hui, H. W. H., Peng, H., & Goh, W. W. B. (2022) · PROTEOMICS · doi:10.1002/pmic.202200092 · Cited by 92 · see citing works
Predicting the structural condition of individual sanitary sewer pipes with random forests
Harvey, R. R., & McBean, E. A. (2014) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-2013-0431 · Cited by 89 · see citing works
A balanced approach to the multi-class imbalance problem
Mosley, L. (2013) · doi:10.31274/etd-180810-3375 · Cited by 86 · see citing works
Xu, Z., Shen, D., Kou, Y., & Nie, T. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3197156 · Cited by 84 · see citing works
Zarkogianni, K., Αθανασίου, Μ., Thanopoulou, A. C., & Nikita, K. S. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2765639 · Cited by 85 · see citing works
“Several techniques have been developed to overcome this problem, such as underand over –sampling, cost-sensitive learning, and ensemble learning [21].”
MethodKraiem, M. S., Sánchez, F., & Garcı́a, M. N. M. (2021) · Applied Sciences · doi:10.3390/app11188546 · Cited by 81 · see citing works
“The work of [30] includes an effective framework that integrates an evaluation measure into the objective function of cost-sensitive SVM directly to increase the performance by improving misclassification cost criteria.”
MethodA Review on Solution to Class Imbalance Problem: Undersampling Approaches
Devi, D., Biswas, S. K., & Purkayastha, B. (2020) · International Conference on Computational Performance Evaluation (ComPE) · doi:10.1109/compe49325.2020.9200087 · Cited by 98 · see citing works
“Numerous solutions are proposed by researchers over the years for handling the issue which are mainly categorized in three major categories: [a] Data level solutions , [b] Algorithmic Solutions and [c] Ensemble learning based solutions [7] [8] [9].”
BackgroundDeep learning architectures for Parkinson's disease detection by using multi-modal features
Pahuja, G., & Prasad, B. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105610 · Cited by 82 · see citing works
Minissi, M. E., Giglioli, I. A. C., Mantovani, F., & Alcañíz, M. (2021) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-021-05106-5 · Cited by 74 · see citing works
“However, the TD group was three times bigger than the ASD group, hence the imbalanced sample affected the ML accuracy score, which tended to be biased towards the sample group with more elements (Nguyen et al., 2009).”
Sielenou, P. D., Viallon-Galinier, L., Hagenmuller, P., Naveau, P., Morin, S., Dumont, M., Verfaillie, D., & Eckert, N. (2021) · Cold Regions Science and Technology · doi:10.1016/j.coldregions.2021.103276 · Cited by 71 · see citing works
AUC4.5: AUC-Based C4.5 Decision Tree Algorithm for Imbalanced Data Classification
Lee, J. (2019) · IEEE Access · doi:10.1109/access.2019.2931865 · Cited by 67 · see citing works
“INTRODUCTION Traditional classification techniques such as logistic regression, decision tree, support vector machines, and neural networks have been developed and modified under the assumption that data has a balanced class distribution [21].”
BackgroundCross project defect prediction using class distribution estimation and oversampling
Limsettho, N., Bennin, K. E., Keung, J., et al. (2018) · Information and Software Technology · doi:10.1016/j.infsof.2018.04.001 · Cited by 64 · see citing works
DBMUTE: density-based majority under-sampling technique
Bunkhumpornpat, C., & Sinapiromsaran, K. (2016) · Knowledge and Information Systems · doi:10.1007/s10115-016-0957-5 · Cited by 63 · see citing works
An Approach for Detecting Spam in Arabic Opinion Reviews
Hammad, A. A., & El-Halees, A. M. (2015) · The International Arab Journal of Information Technology · Cited by 61 · see citing works
Bunkhumpornpat, C., Boonchieng, E., Chouvatut, V., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101073 · Cited by 57 · see citing works
Non-Destructive Assessment of Chicken Egg Fertility
Adegbenjo, A., Liu, L., & Ngadi, M. O. (2020) · Sensors · doi:10.3390/s20195546 · Cited by 61 · see citing works
“This criterion has, however, been regarded as misleading when considering the data set of an imbalanced nature [114], in which case, the choice of the confusion matrix evaluation criterion is considered more appropriate.”
BackgroundOn the Classification of Imbalanced Datasets
KumarM.N, A., & Sheshadri, H. S. (2012) · International Journal of Computer Applications · doi:10.5120/6280-8449 · Cited by 55 · see citing works
Gurevich, P., Stuke, H., Kastrup, A., Stuke, H., & Hildebrandt, H. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00114 · Cited by 53 · see citing works
“…avoid artificially high accuracy values in the case of an unbalanced number of cases per class (i.e., to avoid a high sensitivity at the expense of poor specificity or vice versa (Nguyen et al., 2009)), the algorithm was trained to obtain a maximal harmonic mean between sensitivity and specificity.”
MethodA Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment
Biswas, S. K., Devi, D., & Chakraborty, M. (2018) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2018100107 · Cited by 53 · see citing works
Chen, X., Lin, X., Shen, Q., & Qian, X. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3035789 · Cited by 53 · see citing works
“However, when the number of samples in the minor class is not adequate, the probability distribution estimate may not be accurate [26].”
BackgroundA two-stage fuzzy neural approach for credit risk assessment in a Brazilian credit card company
Fonseca, D. P., Wänke, P., & Corrêa, H. L. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106329 · Cited by 48 · see citing works
Identifying Illicit Addresses in Bitcoin Network
Li, Y., Cai, Y., Tian, H., Xue, G., & Zheng, Z. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-9213-3_8 · Cited by 53 · see citing works
Bennin, K. E., Keung, J., Monden, A., et al. (2016) · IEEE Annual Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2016.144 · Cited by 47 · see citing works
“[38], [41], [39], [46] just to name a few; however, these measures ignore the effort needed to fix faults, i.”
BackgroundMethodZhao, M., Edakunni, N. U., Pocock, A., & Gavin Brown, B. (2013) · Research Explorer (The University of Manchester) · Cited by 45 · see citing works
Enhancing techniques for learning decision trees from imbalanced data
Chaabane, I., Guermazi, R., & Hammami, M. (2019) · Advances in Data Analysis and Classification · doi:10.1007/s11634-019-00354-x · Cited by 37 · see citing works
A Neighborhood Undersampling Stacked Ensemble (NUS-SE) in imbalanced classification
Seng, Z., Kareem, S. A., & Varathan, K. D. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114246 · Cited by 37 · see citing works
Sutoyo, E., & Fadlurrahman, M. A. (2020) · Jurnal Edukasi dan Penelitian Informatika (JEPIN) · doi:10.26418/jp.v6i3.42896 · Cited by 37 · see citing works
Saini, M., & Susan, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31332-6_36 · Cited by 36 · see citing works
Classification success of six machine learning algorithms in radar ornithology
Rosa, I. M. D., Marques, A. T., Palminha, G., et al. (2015) · Ibis · doi:10.1111/ibi.12333 · Cited by 36 · see citing works
High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM
Stegmayer, G. S., Yones, C., Kamenetzky, L., & Milone, D. H. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2576459 · Cited by 33 · see citing works
PatchNet: Hierarchical Deep Learning-Based Stable Patch Identification for the Linux Kernel
Hoang, T., Lawall, J., Tian, Y., Oentaryo, R. J., & Lo, D. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2952614 · Cited by 38 · see citing works
“Again we only use AUC as this dataset is highly imbalanced [49], [53], [60].”
MethodHandling Imbalanced Ratio for Class Imbalance Problem Using SMOTE
Noorhalim, N., Ali, A., & Shamsuddin, S. M. (2019) · Springer eBooks · doi:10.1007/978-981-13-7279-7_3 · Cited by 34 · see citing works
Ejlskov, L., Wulff, J. N., Kalkbrenner, A. E., Ladd‐Acosta, C., Fallin, M. D., Agerbo, E., Mortensen, P. B., Lee, B. K., & Schendel, D. (2021) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.04.007 · Cited by 35 · see citing works
Zhang, Y., & Ding, Z. J. (2011) · Cited by 32 · see citing works
A Wearable Sleep Position Tracking System Based on Dynamic State Transition Framework
Jeon, S., Park, T., Paul, A., Lee, Y., & Son, S. H. (2019) · IEEE Access · doi:10.1109/access.2019.2942608 · Cited by 33 · see citing works
Zhu, X., Cheng, Y., Wu, J., Hu, R., & Cui, X. K. (2019) · IEEE Access · doi:10.1109/access.2019.2961742 · Cited by 33 · see citing works
“Therefore, the test data can be used to quantitatively evaluate the performance of the detection or classification methods by some general indicators, such as precision, recall and F-measure [47].”
MethodReview spam detection using active learning
Ahsan, M. N. I., Nahian, T., Kafi, A. A., Hossain, M. I., & Shah, F. M. (2016) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon.2016.7746279 · Cited by 29 · see citing works
Lu, C., Lubin, J. H., Sarma, V., Stentz, S. Z., Wang, G., Wang, S., & Khare, S. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2303590120 · Cited by 28 · see citing works
“We evaluated PGCN performance using different metrics besides accuracy, including F1 score, Precision, Recall, Area under curve (AUC), and Average Precision (AP), all standard evaluation metrics for ML tasks with imbalanced data (47–50).”
MethodUhl, J. H., Zoraghein, H., Leyk, S., et al. (2018) · International Journal of Digital Earth · doi:10.1080/17538947.2018.1550120 · Cited by 27 · see citing works
“…of the different GHSL data layers is compared pixel-wise to the reference data and confusion matrices are created to derive various accuracy measures (Fielding and Bell 1997; Nguyen, Bouzerdoum, and Phung 2009) overall and for the different strata of development intensity across the 31 counties.”
MethodStringhini, G., Shen, Y. C., Han, Y., et al. (2017) · Annual Computer Security Applications Conference · doi:10.1145/3134600.3134604 · Cited by 26 · see citing works
Chennuru, V. K., & Timmappareddy, S. R. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02369-4 · Cited by 26 · see citing works
TLUSBoost algorithm: a boosting solution for class imbalance problem
Kumar, S., Biswas, S. K., & Devi, D. (2018) · Soft Computing · doi:10.1007/s00500-018-3629-4 · Cited by 25 · see citing works
Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR
Zhao, X., Shi, S., Yang, J. S., et al. (2020) · Sensors · doi:10.3390/s20030935 · Cited by 25 · see citing works
Optimizing Android Malware Detection Via Ensemble Learning
Abikoye, O. C., Gyunka, B. A., & Oluwatobi, A. N. (2020) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v14i09.11548 · Cited by 25 · see citing works
Bagui, S. S., Bagui, S. S., Mink, D., et al. (2023) · Computers · doi:10.3390/computers12100204 · Cited by 24 · see citing works
Ahmed, J., & Green, R. (2024) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2023.107339 · Cited by 24 · see citing works
Modified Adaptive Synthetic SMOTE to Improve Classification Performance in Imbalanced Datasets
Gameng, H. A., Gerardo, B. D., & Medina, R. P. (2019) · IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS) · doi:10.1109/icetas48360.2019.9117287 · Cited by 24 · see citing works
An Improved Method for Training Data Selection for Cross-Project Defect Prediction
Bhat, N. A., & Farooq, S. U. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06088-3 · Cited by 22 · see citing works
“To handle the class imbalance, researchers [33,34] applied the sampling techniques of over and under sampling [35,36].”
MethodNear-infrared hyperspectral imaging for evaluation of aflatoxin contamination in corn kernels
Tao, F., Yao, H., Hruska, Z., et al. (2022) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2022.07.002 · Cited by 22 · see citing works
Salarian, A. A., Etemadfard, H., Rahimzadegan, A., & Ghalehnovi, M. (2022) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002406 · Cited by 22 · see citing works
Yousef, A., & Charkari, N. M. (2013) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2013.07.001 · Cited by 21 · see citing works
Copiaco, A., Ritz, C. H., Fasciani, S., et al. (2019) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit47144.2019.9001814 · Cited by 20 · see citing works
“The F1-score is defined by (1), where recall and precision are given by (2) and (3) [28].”
BackgroundCopiaco, A., Ritz, C. H., Abdulaziz, N., et al. (2021) · Applied Sciences · doi:10.3390/app11114880 · Cited by 20 · see citing works
“This is defined to be a measure that takes into consideration both the recall and the precision, which are derived from the ratios of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN) [68], which can be extracted from confusion matrices.”
BackgroundPredicting severely imbalanced data disk drive failures with machine learning models
Ahmed, J., & Green, R. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100361 · Cited by 19 · see citing works
Methods of Handling Unbalanced Datasets in Credit Card Fraud Detection
MINASTIREANU, E.-A., & Meșniță, G. (2020) · BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE · doi:10.18662/brain/11.1/19 · Cited by 18 · see citing works
“The literature (Nguyen et al., 2009; Haibo & Edwardo, 2009; Chris & Robert, 2000; Charles et al., 2004) presents several ways of incorporating cost into decision tree classification, like: one “cost can be used in order to tune the decision threshold, another one can be applied in splitting…”
BackgroundValidating Requirements Reviews by Introducing Fault-Type Level Granularity
Singh, M., Anu, V. K., Walia, G., et al. (2018) · Innovations in Software Engineering Conference (ISEC) · doi:10.1145/3172871.3172880 · Cited by 16 · see citing works
A Survey on Class Imbalance Learning on Automatic Visual Inspection
Mera, C., & Branch, J. W. (2014) · IEEE Latin America Transactions · doi:10.1109/tla.2014.6868867 · Cited by 17 · see citing works
Ensemble methods in bank direct marketing
Pan, Y., & Tang, Z. (2014) · International Conference on Service Systems and Service Management · doi:10.1109/icsssm.2014.6874056 · Cited by 15 · see citing works
“[18] also detailed the problems associated with class imbalance in learning (such as lack of data and concept complexity) and proposed new learning algorithm for imbalanced data set.”
BackgroundEnsemble Learning For Imbalanced Data Classification Problem
Chujai, P., Chomboon, K., Teerarassamee, P., et al. (2015) · International Conference on Industrial Application Engineering (ICIAE) · doi:10.12792/iciae2015.079 · Cited by 15 · see citing works
On the Classification of Imbalanced Datasets
Rani, T. S. (2011) · Cited by 15 · see citing works
Detecting Developers' Task Switches and Types
Meyer, A. N., Satterfield, C., Zuger, M., Kevic, K., Murphy, G. C., Zimmermann, T., & Fritz, T. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.2984086 · Cited by 14 · see citing works
“classes have a limited number of samples [58].”
BackgroundHou, G., Jia, L., Zhang, Y., Wu, W. D., Zhao, L., Zhao, J., Wang, L., & Qiang, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106140 · Cited by 14 · see citing works
CCBERT: Self-Supervised Code Change Representation Learning
Zhou, X., Xu, B., Han, D., Yang, Z., He, J., & Lo, D. (2023) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme58846.2023.00028 · Cited by 17 · see citing works
“We follow prior work [1], [5], [28] to avoid using threshold-dependent measures (e.g., accuracy or F1-scores) since these measures strongly depend on arbitrary thresholds [29], [30].”
MethodCredibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
Tong, H., Wang, S., & Li, G. (2020) · Applied Sciences · doi:10.3390/app10228059 · Cited by 14 · see citing works
“In the machine learning field, class-imbalance learning methods can be divided into two categories [68]: data-level methods and algorithm-level methods.”
MethodTsai, C.-Y., Majumdar, A., Wang, Y., et al. (2022) · International Journal of Occupational Safety and Ergonomics · doi:10.1080/10803548.2022.2135281 · Cited by 14 · see citing works
“Although we used the SMOTE to compensate for the data imbalance, the generated data may still lack variation, which potentially caused the low sensitivity of developed models [45].”
MethodStacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data
Alhassan, Z., Budgen, D., Alshammari, R., et al. (2018) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2018.00087 · Cited by 14 · see citing works
“AUC gives same weights for both minority and majority classes [19].”
BackgroundDecentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters
Soliman, A., Girdzijauskas, Š., Bouguelia, M.-R., Pashami, S., & Nowaczyk, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47426-3_27 · Cited by 14 · see citing works
“A lot of work has been done for solving class imbalance and missing classes using data resampling, however most of these methods require access to the whole data, which is not applicable in decentralized systems [17] .”
BackgroundEnd-of-line fault detection for combustion engines using one-class classification
Leitner, L., Lagrange, A., & Endisch, C. (2016) · IEEE International Conference on Advanced Intelligent Mechatronics (AIM) · doi:10.1109/aim.2016.7576768 · Cited by 13 · see citing works
“Trained on a dataset with such strong class imbalance, standard binary classification algorithms like decision trees or multilayer perceptrons (MLP) have a high bias towards the majority class [3], [4].”
MethodSultan, N., Hasan, M., Wahid, M. F., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3303342 · Cited by 12 · see citing works
“The minority class is oversampled using SMOTE, and samples from the majority classes are subtracted using Tomek Links, a technique for determining pairings of samples from various classes that are close to one another [22].”
MethodUnderwater object detection in noisy imbalanced datasets
Chen, L., Li, T., Zhou, A., et al. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110649 · Cited by 12 · see citing works
Scout Particle Swarm Optimization
Koyuncu, H., & Ceylan, R. (2014) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-11128-5_21 · Cited by 12 · see citing works
SkewBoost: An algorithm for classifying imbalanced datasets
Hukerikar, S., Tumma, A., Nikam, A., et al. (2011) · International Conference on Computer and Communication Technology · doi:10.1109/iccct.2011.6075185 · Cited by 12 · see citing works
Sentiment Analysis Based Fuzzy Decision Platform for the Saudi Stock Market
Hasan, A. A., & Fong, A. C. M. (2018) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2018.8500292 · Cited by 12 · see citing works
“The balanced data gives less accuracy than the unbalanced, which is expected as the imbalance ratio (defined by [16] as minority class/majority class) is high.”
BackgroundMethodAhmad, F. K., Al-Qammaz, A. Y., & Yusof, Y. (2016) · Jurnal Teknologi · doi:10.11113/jt.v78.8842 · Cited by 12 · see citing works
“positive and negatives points respectively [38].”
Mohapatra, U. M., & Tripathy, S. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i1.pp487-499 · Cited by 11 · see citing works
Yehuala, T. Z., Derseh, N. M., Tewelgne, M. F., et al. (2024) · Journal of Epidemiology and Global Health · doi:10.1007/s44197-024-00259-9 · Cited by 10 · see citing works
Handling the Imbalanced Problem in Agri-Food Data Analysis
Adegbenjo, A., & Ngadi, M. O. (2024) · Foods · doi:10.3390/foods13203300 · Cited by 10 · see citing works
Wang, J., Liu, J., DiStefano, C., et al. (2020) · Journal of Psychoeducational Assessment · doi:10.1177/0734282920951727 · Cited by 10 · see citing works
“Oversampling could be used to deal with the imbalanced data feature (e.g., Ali et al., 2015; He & Garcia, 2008; Nguyen et al., 2009) by increasing the sample size of the minority group (i.e., positive cases with EBD) with simulation to achieve a more balanced sample.”
BackgroundInfluentialHellinger distance based oversampling method to solve multi-class imbalance problem
Kumari, A., & Thakar, U. (2017) · International Conference on Communication Systems and Network Technologies · doi:10.1109/csnt.2017.8418525 · Cited by 10 · see citing works
Learning from evolving video streams in a multi-camera scenario
Khoshrou, S., Cardoso, J. S., & Teixeira, L. F. (2015) · Machine Learning · doi:10.1007/s10994-015-5515-y · Cited by 10 · see citing works
“…2 Overlapped OneStopMoveEnter1 42 (10 − 555) 14 0.14 2 Overlapped PETS2009 19 (85 − 576) 10 0.13 2 Overlapped SAIVT-SOFTBIO 33 (21 − 211) 11 0.12 8 Overlpped, nonoverlpped Imbalance degree (Nguyen et al. 2009) is defined by the ratio of sample size of minority class to that of the majority ones…”
BackgroundInfluentialImprovement in Boosting Method by Using RUSTBoost Technique for Class Imbalanced Data
Kumar, A., Bharti, R., Gupta, D., et al. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1280-9_5 · Cited by 10 · see citing works
Tao, F., Yao, H., Hruska, Z., et al. (2024) · Journal of Food Protection · doi:10.1016/j.jfp.2024.100335 · Cited by 9 · see citing works
Babiloni, F., Cichocki, A. S., Sanei, S., Laura Astolfi, A., Febo Cincotti, A., & Andino, S. L. G. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/913040 · Cited by 9 · see citing works
- Authors
- Hoang, G., Bouzerdoum, A., & Lam, S.
- Venue
- Pattern Recognition
- Publisher
- InTech
- Published
- 2009-10-01
- DOI
- 10.5772/7544
- OpenAlex
- W2130745297
- Cited by
- 178
- References
- 0
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 16 · 2023: 11 · 2022: 16 · 2021: 22 · 2020: 21 · 2019: 15 · 2018: 14 · 2017: 11 · 2016: 10 · 2015: 9 · 2014: 7 · 2013: 10 · 2012: 4
- FWCI
- 6.47
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- InTech eBooks (publishedVersion, cc-by-nc-sa)
- Research Online (University of Wollongong) (submittedVersion)
- BiblioBoard Library Catalog (Open Research Library) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion)
- IntechOpen (submittedVersion, cc-by-nc-sa)
- Publikationsserver des Ibero-Amerikanischen Instituts Preussischer Kulturbesitz Berlin
- 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 › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- pattern classification, class imbalance
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
