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.
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Patterson, G., Zhang, M. (2007). Fitness Functions in Genetic Programming for Classification with Unbalanced Data. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_90
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DOI: https://doi.org/10.1007/978-3-540-76928-6_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76926-2
Online ISBN: 978-3-540-76928-6
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