SMOTified-GAN for class imbalanced pattern classification problems

TL;DR

This paper introduces SMOTified-GAN, a hybrid oversampling method combining SMOTE and GAN to improve minority class sample quality and classifier performance in imbalanced datasets.

Contribution

It proposes a novel two-phase oversampling approach that uses GAN to refine SMOTE-generated samples, enhancing data realism and classification metrics.

Findings

Sample quality of minority classes improved in benchmark datasets.

F1-score increased by up to 9% over existing algorithms.

Time complexity remains reasonable at approximately O(N^2d^2T).

Abstract

Class imbalance in a dataset is a major problem for classifiers that results in poor prediction with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive training dataset. Generally, the pre-processing technique of oversampling of minority class(es) are used to overcome this deficiency. Our focus is on using the hybridization of Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalanced problems. We propose a novel two-phase oversampling approach involving knowledge transfer that has the synergy of SMOTE and GAN. The unrealistic or overgeneralized samples of SMOTE are transformed into realistic distribution of data by GAN where there is not enough minority class data available for GAN to process them by itself effectively. We named it SMOTified-GAN as GAN works on pre-sampled minority data produced by SMOTE rather than randomly generating the samples itself. The experimental results prove the sample quality of minority class(es) has been improved in a variety of tested benchmark datasets. Its performance is improved by up to 9\% from the next best algorithm tested on F1-score measurements. Its time complexity is also reasonable which is around $O(N^2d^2T)$ for a sequential algorithm.