WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning

TL;DR

WOTBoost introduces a novel weighted oversampling method integrated with boosting to enhance minority class classification in imbalanced datasets, outperforming existing techniques on multiple metrics across diverse datasets.

Contribution

The paper proposes WOTBoost, a new oversampling technique combined with boosting that adaptively synthesizes minority samples to improve classification in imbalanced data.

Findings

WOTBoost achieves the best G mean on 6 datasets.

WOTBoost attains the highest AUC score on 7 datasets.

Outperforms several existing methods in imbalanced learning scenarios.

Abstract

Machine learning classifiers often stumble over imbalanced datasets where classes are not equally represented. This inherent bias towards the majority class may result in low accuracy in labeling minority class. Imbalanced learning is prevalent in many real-world applications, such as medical research, network intrusion detection, and fraud detection in credit card transactions, etc. A good number of research works have been reported to tackle this challenging problem. For example, Synthetic Minority Over-sampling TEchnique (SMOTE) and ADAptive SYNthetic sampling approach (ADASYN) use oversampling techniques to balance the skewed datasets. In this paper, we propose a novel method that combines a Weighted Oversampling Technique and ensemble Boosting method (WOTBoost) to improve the classification accuracy of minority data without sacrificing the accuracy of the majority class. WOTBoost adjusts its oversampling strategy at each round of boosting to synthesize more targeted minority data samples. The adjustment is enforced using a weighted distribution. We compare WOTBoost with other four classification models (i.e., decision tree, SMOTE + decision tree, ADASYN + decision tree, SMOTEBoost) extensively on 18 public accessible imbalanced datasets. WOTBoost achieves the best G mean on 6 datasets and highest AUC score on 7 datasets.