A Novel Hybrid Sampling Framework for Imbalanced Learning

Abstract

Class imbalance is a frequently occurring scenario in classification tasks. Learning from imbalanced data poses a major challenge, which has instigated a lot of research in this area. Data preprocessing using sampling techniques is a standard approach to deal with the imbalance present in the data. Since standard classification algorithms do not perform well on imbalanced data, the dataset needs to be adequately balanced before training. This can be accomplished by oversampling the minority class or undersampling the majority class. In this study, a novel hybrid sampling algorithm has been proposed. To overcome the limitations of the sampling techniques while ensuring the quality of the retained sampled dataset, a sophisticated framework has been developed to properly combine three different sampling techniques. Neighborhood Cleaning rule is first applied to reduce the imbalance. Random undersampling is then strategically coupled with the SMOTE algorithm to obtain an optimal balance in the dataset. This proposed hybrid methodology, termed "SMOTE-RUS-NC", has been compared with other state-of-the-art sampling techniques. The strategy is further incorporated into the ensemble learning framework to obtain a more robust classification algorithm, termed "SRN-BRF". Rigorous experimentation has been conducted on 26 imbalanced datasets with varying degrees of imbalance. In virtually all datasets, the proposed two algorithms outperformed existing sampling strategies, in many cases by a substantial margin. Especially in highly imbalanced datasets where popular sampling techniques failed utterly, they achieved unparalleled performance. The superior results obtained demonstrate the efficacy of the proposed models and their potential to be powerful sampling algorithms in imbalanced domain.