A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios

TL;DR

This paper introduces a three-step adaptive oversampling method that enhances minority class representation in imbalanced datasets, leading to improved classification performance across various domains.

Contribution

It proposes a novel oversampling technique combining SMOTE, Gaussian-Mixture clustering, and cluster-weighting to better address class imbalance.

Findings

Outperforms original SMOTE in multiple datasets

Reduces classifier bias towards majority class

Enhances minority class detection accuracy

Abstract

Imbalance in the proportion of training samples belonging to different classes often poses performance degradation of conventional classifiers. This is primarily due to the tendency of the classifier to be biased towards the majority classes in the imbalanced dataset. In this paper, we propose a novel three step technique to address imbalanced data. As a first step we significantly oversample the minority class distribution by employing the traditional Synthetic Minority OverSampling Technique (SMOTE) algorithm using the neighborhood of the minority class samples and in the next step we partition the generated samples using a Gaussian-Mixture Model based clustering algorithm. In the final step synthetic data samples are chosen based on the weight associated with the cluster, the weight itself being determined by the distribution of the majority class samples. Extensive experiments on several standard datasets from diverse domains shows the usefulness of the proposed technique in comparison with the original SMOTE and its state-of-the-art variants algorithms.