Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Machine Learning
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Journal of Artificial Intelligence Research
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AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
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ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
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IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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IEEE Transactions on Evolutionary Computation
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AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
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Genetic programming for classification with unbalanced data
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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This paper investigates improvements to the fitness function in Genetic Programming to better solve binary classification problems with unbalanced data. Data sets are unbalanced when there is a majority of examples for one particular class over the other class(es). We show that using overall classification accuracy as the fitness function evolves classifiers with a performance bias toward the majority class at the expense of minority class performance. We develop four new fitness functions which consider the accuracy of majority and minority class separately to address this learning bias. Results using these fitness functions show that good accuracy for both the minority and majority classes can be achieved from evolved classifiers while keeping overall performance high and balanced across the two classes.