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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Patterson, G., & Zhang, M. (2007). Fitness functions in genetic programming for classification with unbalanced data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4830 LNAI, pp. 769–775). https://doi.org/10.1007/978-3-540-76928-6_90
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