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

  • Van Loi Cao
  • Nhien-An Le-Khac
  • Michael O’Neill
  • Miguel Nicolau
  • James McDermott
Conference paper

DOI: 10.1007/978-3-319-31204-0_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9597)
Cite this paper as:
Cao V.L., Le-Khac NA., O’Neill M., Nicolau M., McDermott J. (2016) Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data. In: Squillero G., Burelli P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science, vol 9597. Springer, Cham

Abstract

Credit card classification based on machine learning has 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 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 classifiers 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.

Keywords

Class imbalance Credit card data Fitness functions 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Van Loi Cao
    • 1
  • Nhien-An Le-Khac
    • 1
  • Michael O’Neill
    • 1
  • Miguel Nicolau
    • 1
  • James McDermott
    • 1
  1. 1.Natural Computing Research and Application GroupUniversity College DublinDublinIreland

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