Gamma distribution-based sampling for imbalanced data

TL;DR

This paper introduces a gamma distribution-based resampling method to address class imbalance, demonstrating superior performance over existing techniques on multiple datasets.

Contribution

A novel resampling technique using gamma distribution for generating minority class instances, improving data balancing in imbalanced classification tasks.

Findings

Outperforms state-of-the-art methods on 12 of 24 datasets

Achieves best results on diverse real and synthetic datasets

Surpasses SMOTE in effectiveness on multiple benchmarks

Abstract

Imbalanced class distribution is a common problem in a number of fields including medical diagnostics, fraud detection, and others. It causes bias in classification algorithms leading to poor performance on the minority class data. In this paper, we propose a novel method for balancing the class distribution in data through intelligent resampling of the minority class instances. The proposed method is based on generating new minority instances in the neighborhood of the existing minority points via a gamma distribution. Our method offers a natural and coherent approach to balancing the data. We conduct a comprehensive numerical analysis of the new sampling technique. The experimental results show that the proposed method outperforms the existing state-of-the-art methods for imbalanced data. Concretely, the new sampling technique produces the best results on 12 out of 24 real life as well as synthetic datasets. For comparison, the SMOTE method achieves the top score on only 1 dataset. We conclude that the new technique offers a simple yet effective sampling approach to balance data.