Learning from class-imbalanced data: Review of methods and applications

SMOTE synthetic minority over-sampling technique

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568

A decision-theoretic generalization of on-line learning and an application to boosting

Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466

Logistic Regression in Rare Events Data

King, G., & Zeng, L. (2001) · Political Analysis · doi:10.1093/oxfordjournals.pan.a004868 · Cited by 4,316

A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

Hand, D. J., & Till, R. J. (2001) · Machine Learning · doi:10.1023/a:1010920819831 · Cited by 2,276

Mining with rarity: a unifying framework

Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393

A Survey of Predictive Modeling on Imbalanced Domains

Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129

Building Markov state models with solvent dynamics

Gu, C., Chang, H.-W., Maibaum, L., Pande, V. S., Carlsson, G. E., & Guibas, L. J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s2-s8 · Cited by 21

A systematic study of the class imbalance problem in convolutional neural networks

Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088

Deep learning-based electroencephalography analysis: a systematic review

Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532

Artificial intelligence, bias and clinical safety

Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008370 · Cited by 956

Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919012117 · Cited by 685

Machine learning and integrative analysis of biomedical big data

Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

Khushi, M., Shaukat, K., Alam, T. M., Hameed, I. A., Uddin, S., Luo, S., Yang, X., & Reyes, M. C. (2021) · IEEE Access · doi:10.1109/access.2021.3102399 · Cited by 335

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

Oviedo, F., Ren, Z., Sun, S., Settens, C., Liu, Z., Hartono, N. T. P., Ramasamy, S., DeCost, B. L., Tian, S. I. P., Romano, G., et al. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0196-x · Cited by 327

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Ashmore, R., Calinescu, R., & Paterson, C. (2022) · ACM Computing Surveys · doi:10.1145/3453444 · Cited by 256

A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset

Liu, T., Fan, W., & Wu, C. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101723 · Cited by 220

Interpretable machine learning for imbalanced credit scoring datasets

Chen, Y., Calabrese, R., & Martin-Barragan, B. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.036 · Cited by 190

Machine-learning-based patient-specific prediction models for knee osteoarthritis

Jamshidi, A., Pelletier, J.-P., & Martel-Pelletier, J. (2019) · Nature Reviews Rheumatology · doi:10.1038/s41584-018-0130-5 · Cited by 190

Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients

Deng, Y., Lu, L., Aponte, L., Angelidi, A. M., Novak, V., Karniadakis, G. E., & Mantzoros, C. S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00480-x · Cited by 152

Deep learning predictions of galaxy merger stage and the importance of observational realism

Bottrell, C., Hani, M. H., Teimoorinia, H., Ellison, S. L., Moreno, J., Torrey, P., Hayward, C. C., Thorp, M., Simard, L., & Hernquist, L. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2934 · Cited by 113

Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades

Condran, S., Bewong, M., Islam, M. Z., Maphosa, L., & Zheng, L. (2022) · IEEE Access · doi:10.1109/access.2022.3188649 · Cited by 99

Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

Wu, B., Liu, L., Yang, Y., Zheng, K., & Wang, X. (2020) · IEEE Access · doi:10.1109/access.2020.2975630 · Cited by 54

Taxonomy of Machine Learning Safety: A Survey and Primer

Mohseni, S., Wang, H., Xiao, C., Yu, Z., Wang, Z., & Yadawa, J. (2023) · ACM Computing Surveys · doi:10.1145/3551385 · Cited by 53

A multiclass classification using one-versus-all approach with the differential partition sampling ensemble

Gao, X., He, Y., Zhang, M., Diao, X., Jing, X., Ren, B., & Ji, W. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.104034 · Cited by 44

SGBBA: An Efficient Method for Prediction System in Machine Learning using Imbalance Dataset

Islam, S., Sara, U., Kawsar, A., Rahman, A., Kundu, D., Das, D., Rezaul, A. N. M., & Hasan, M. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120351 · Cited by 37

Factors Associated With Semaglutide Initiation Among Adults With Obesity

Podolsky, M. I., Raquib, R., Shafer, P. R., Hempstead, K., Ellis, R. P., & Stokes, A. C. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.55222 · Cited by 26

Insider Threat Detection: Machine Learning Way

Raval, M. S., Gandhi, R., & Chaudhary, S. (2018) · Advances in Information Security · doi:10.1007/978-3-319-97643-3_2 · Cited by 24

Enhancing Debris Flow Warning via Machine Learning Feature Reduction and Model Selection

Zhou, Q., Tang, H., Hibert, C., Chmiel, M., Walter, F., Dietze, M., & Turowski, J. M. (2025) · Journal of Geophysical Research: Earth Surface · doi:10.1029/2024jf008094 · Cited by 20

Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data

Pan, Y., Xie, F., & Zhao, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3256442 · Cited by 17

Classification with LSTM Networks in User Behaviour Analytics with Unbalanced Environment

Parshutin, S., Kirshners, A., Kornijenko, Y., Zabiniako, V., Gasparovica-Asite, M., & Rozkalns, A. (2021) · Automatic Control and Computer Sciences · doi:10.3103/s0146411621010077 · Cited by 7