A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data

Ensemble technique and under-sampling technique are both effective tools used for imbalanced dataset classification problems. In this paper, a novel ensemble method combining the advantages of both ensemble learning for biasing classifiers and a new under-sampling method is proposed. The under-sampling method is named Binary PSO instance selection; it gathers with ensemble classifiers to find the most suitable length and combination of the majority class samples to build a new dataset with minority class samples. The proposed method adopts multi-objective strategy, and contribution of this method is a notable improvement of the performances of imbalanced classification, and in the meantime guaranteeing a best integrity possible for the original dataset. We experimented the proposed method and compared its performance of processing imbalanced datasets with several other conventional basic ensemble methods. Experiment is also conducted on these imbalanced datasets using an improved version where ensemble classifiers are wrapped in the Binary PSO instance selection. According to experimental results, our proposed methods outperform single ensemble methods, state-of-the-art under-sampling methods, and also combinations of these methods with the traditional PSO instance selection algorithm.

Automated summary

This paper introduces a novel ensemble method that combines the advantages of ensemble learning and under-sampling by using a multi-objective strategy, resulting in significantly improved performance in imbalanced classification while maintaining the integrity of the original dataset. The proposed method outperforms single ensemble methods, state-of-the-art under-sampling methods, and combinations of these methods with the traditional PSO instance selection algorithm according to experimental results.