Abstract
Class imbalance problem has been a widely studied problem in data mining. In this paper, we present a new filter approach to the class imbalance problem that uses repeated under-sampling to create balanced data sets and then uses majority voting ensemble type learning to create a meta-classifier. We test our method on five imbalanced data sets and compare its performance with that of three other techniques. We show that our method significantly improves the prediction accuracy of the under-represented class while also reducing the gap in prediction accuracy between the two classes.
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Sikora, R., Raina, S. (2019). Controlled Under-Sampling with Majority Voting Ensemble Learning for Class Imbalance Problem. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_3
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DOI: https://doi.org/10.1007/978-3-030-01177-2_3
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