Fitness functions in genetic programming for classification with unbalanced data

Authors:
Grant Patterson;Mengjie Zhang
Affiliations:
School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand;School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

This paper describes a genetic programming (GP) approach to binary classification with class imbalance problems. This approach is examined on two benchmark and two synthetic data sets. The results show that when using the overall classification accuracy as the fitness function, the GP system is strongly biased toward the majority class. Two new fitness functions are developed to deal with the class imbalance problem. The experimental results show that both of them substantially improve the performance for the minority class, and the performance for the majority and minority classes is much more balanced.