Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets

TL;DR

This paper enhances genetic programming classifiers for credit card fraud detection by developing new fitness functions that improve accuracy on both minority and majority classes in imbalanced datasets.

Contribution

It introduces two novel fitness functions for GP that better handle class imbalance, improving classification performance on credit card datasets.

Findings

Proposed fitness functions improve minority class accuracy.

Enhanced GP classifiers perform better on imbalanced credit card datasets.

Results show increased overall and minority class accuracy.

Abstract

Credit card fraud detection based on machine learning has recently attracted considerable interest from the research community. One of the most important tasks in this area is the ability of classifiers to handle the imbalance in credit card data. In this scenario, classifiers tend to yield poor accuracy on the fraud class (minority class) despite realizing high overall accuracy. This is due to the influence of the majority class on traditional training criteria. In this paper, we aim to apply genetic programming to address this issue by adapting existing fitness functions. We examine two fitness functions from previous studies and develop two new fitness functions to evolve GP classifier with superior accuracy on the minority class and overall. Two UCI credit card datasets are used to evaluate the effectiveness of the proposed fitness functions. The results demonstrate that the proposed fitness functions augment GP classifiers, encouraging fitter solutions on both the minority and the majority classes.