Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems

In this article, we propose a novel entropy and confidence-based undersampling boosting (ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is combined with a new undersampling method to improve the generalization performance. To avoid losing informative samples during the data preprocessing of the boosting-based ensemble, both confidence and entropy are used in ECUBoost as benchmarks to ensure the validity and structural distribution of the majority samples during the undersampling. Furthermore, different from other iterative dynamic resampling methods, ECUBoost based on confidence can be applied to algorithms without iterations such as decision trees. Meanwhile, random forests are used as base classifiers in ECUBoost. Furthermore, experimental results on both artificial data sets and KEEL data sets prove the effectiveness of the proposed method.

I. Introduction

The last decade has witnessed the implementation of comprehensive classification for real-world imbalanced problems [34]–[36], [47], such as access security medical problems [1], [2], and e-mail filtering [3]. In an imbalanced data set, the samples of some classes are obviously less numerous than those of other classes. Frequently, the former is usually called minority classes, while the others are majority classes. In general, minority classes are more important. For instance, in access security systems, only a tiny minority of people can be regarded as safe, while the majority should be refused [2].