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Searching for Optimal Ensemble of Feature-classifier Pairs in Gene Expression Profile using Genetic Algorithm  

박찬호 (연세대학교 컴퓨터공학과)
조성배 (연세대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.4, 2004 , pp. 525-536 More about this Journal
Abstract
Gene expression profile is numerical data of gene expression level from organism, measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify disease with gene expression profile. Because all genes are not related to disease, it is needed to select related genes that is called feature selection, and it is needed to classify selected genes properly. This paper Proposes GA based method for searching optimal ensemble of feature-classifier pairs that are composed with seven feature selection methods based on correlation, similarity, and information theory, and six representative classifiers. In experimental results with leave-one-out cross validation on two gene expression Profiles related to cancers, we can find ensembles that produce much superior to all individual feature-classifier fairs for Lymphoma dataset and Colon dataset.
Keywords
GA; gene expression profile; feature selection; classifier; ensemble; GA;
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