Abstract
Learning from imbalanced datasets poses a major research challenge today due to the imbalanced nature of real-world datasets where samples of some entities are few in number, while some other entities have thousands of samples available. A novel hybrid scheme of intelligently oversampling the minority class followed by subsequent intelligent undersampling of the majority class, is proposed in this paper for learning from imbalanced datasets. Different oversampling techniques: SMOTE and the intelligent oversampling versions of Borderline-SMOTE, Adaptive Synthetic Sampling (ADASYN) and MWMOTE, are considered in combination with Sample Subset Optimization (SSO) that is an intelligent majority undersampling technique based on the evolutionary optimization algorithm of Particle Swarm Optimization (PSO). The datasets after balance-correction are applied to the decision tree classifier. Experiments on benchmark datasets from the UCI repository prove the efficiency of our method due to the higher classification accuracies obtained as compared to the baseline methods.
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Susan, S., Kumar, A. (2020). Hybrid of Intelligent Minority Oversampling and PSO-Based Intelligent Majority Undersampling for Learning from Imbalanced Datasets. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_74
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