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Credit card fraud detection based on machine learning has recently attractedconsiderable interest from the research community. One of the most importanttasks in this area is the ability of classifiers to handle the imbalance incredit card data. In this scenario, classifiers tend to yield poor accuracy onthe fraud class (minority class) despite realizing high overall accuracy. Thisis 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 byadapting existing fitness functions. We examine two fitness functions fromprevious studies and develop two new fitness functions to evolve GP classifierwith superior accuracy on the minority class and overall. Two UCI credit carddatasets are used to evaluate the effectiveness of the proposed fitnessfunctions. The results demonstrate that the proposed fitness functions augmentGP classifiers, encouraging fitter solutions on both the minority and themajority classes.

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