Genetic search for optimal ensemble of feature-classifier pairs in DNA gene expression profiles

Gene expression profile is numerical data of gene expression levels from organism, measured on the microarray. In general, each specific tissue indicates different expression level in related genes, so that it is possible to classify disease by gene expression profile. For classification, it is needed to select related genes called feature selection, because all the genes are not useful for classification. We propose GA-based method for searching optimal ensemble of feature-classifier pairs of gene expression profile in seven feature selection methods based on correlation, distance, and information theory, and representative six classifiers. Experimental results on two gene expression profiles related to cancers show that GA finds good solution quickly. Especially, in Lymphoma dataset, GA finds the ensemble of 100% accuracy.