Genetic algorithm optimized feature transformation: a comparison with different classifiers

Authors:
Zhijian Huang;Min Pei;Erik Goodman;Yong Huang;Gaoping Li
Affiliations:
Genetic Algorithms Research and Application Group, Michigan State University, East Lansing, MI;Genetic Algorithms Research and Application Group, Michigan State University, East Lansing, MI;Genetic Algorithms Research and Application Group, Michigan State University, East Lansing, MI;Computer Center, East China Normal University, Shanghai, China;Electrocardiograph Research Lab, Medical College, Fudan University, Shanghai, China
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

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Abstract

When using a Genetic Algorithm (GA) to optimize the feature space of pattern classification problems, the performance improvement is not only determined by the data set used, but also depends on the classifier. This work compares the improvements achieved by GA-optimized feature transformations on several simple classifiers. Some traditional feature transformation techniques, such as Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are also tested to see their effects on the GA optimization. The results based on some real-world data and five benchmark data sets from the UCI repository show that the improvements after GA-optimized feature transformation are in reverse ratio with the original classification rate if the classifier is used alone. It is also shown that performing the PCA and LDA transformations on the feature space prior to the GA optimization improved the final result.