Fitness Functions in Genetic Programming for Classification with Unbalanced Data

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 stro...

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Bibliographic Details
Published inAI 2007: Advances in Artificial Intelligence Vol. 4830; pp. 769 - 775
Main Authors Patterson, Grant, Zhang, Mengjie
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2007
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Data mining
Mathematical theory of computation
Online AccessGet full text
ISBN9783540769262
3540769269
ISSN0302-9743
1611-3349
DOI10.1007/978-3-540-76928-6_90

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Summary: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.
ISBN:9783540769262
3540769269
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-76928-6_90