An Evolutionary Method for Combining Different FeatureSelection Criteria in Microarray Data Classification

摘要

The classification of cancers from gene expression profiles is a challenging research area in bioinformatics since the highdimensionality of microarray data results in irrelevant and redundant information that affects the performance of classification.This paper proposes using an evolutionary algorithm to select relevant gene subsets in order to further use them for theclassification task. This is achieved by combining valuable results from different feature ranking methods into feature pools whosedimensionality is reduced by a wrapper approach involving a genetic algorithm and SVM classifier. Specifically, the GA explores thespace defined by each feature pool looking for solutions that balance the size of the feature subsets and their classification accuracy.Experiments demonstrate that the proposed method provide good results in comparison to different state of art methods for theclassification of microarray data.