CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification

TL;DR

This paper introduces CUSBoost, a novel clustering-based under-sampling method combined with boosting, which improves classification accuracy on highly imbalanced datasets by effectively reducing bias towards the majority class.

Contribution

The paper proposes CUSBoost, a new ensemble learning approach that integrates clustering-based under-sampling with AdaBoost, outperforming existing methods like RUSBoost and SMOTEBoost.

Findings

CUSBoost outperforms state-of-the-art ensemble methods on various datasets.

It effectively handles highly imbalanced datasets with improved accuracy.

Experimental results validate its robustness across multiple imbalance ratios.

Abstract

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater interest than the majority class instances in real-life applications. Recently, several techniques based on sampling methods (under-sampling of the majority class and over-sampling the minority class), cost-sensitive learning methods, and ensemble learning have been used in the literature for classifying imbalanced datasets. In this paper, we introduce a new clustering-based under-sampling approach with boosting (AdaBoost) algorithm, called CUSBoost, for effective imbalanced classification. The proposed algorithm provides an alternative to RUSBoost (random under-sampling with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost) algorithms. We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 13 imbalance binary and multi-class datasets with various imbalance ratios. The experimental results show that the CUSBoost is a promising and effective approach for dealing with highly imbalanced datasets.