Effective Gene Selection Method Using Bayesian Discriminant Based Criterion and Genetic Algorithms

Abstract

Microarray gene expression data usually consist of a large amount of genes. Among these genes, only a small fraction is informative for performing cancer diagnostic tests. This paper focuses on effective identification of informative genes. A newly developed gene selection criterion using the concept of Bayesian discriminant is used. The criterion measures the classification ability of a feature set. Excellent gene selection results are then made possible. Apart from the cost function, this paper addresses the drawback of conventional sequential forward search (SFS) method. New genetic algorithms based Bayesian discriminant criterion is designed. The proposed strategies have been thoroughly evaluated on three kinds of cancer diagnoses based on the classification results of three typical classifiers which are a multilayer perception model (MLP), a support vector machine model (SVM), and a 3-nearest neighbor rule classifier (3-NN). The obtained results show that the proposed strategies can improve the performance of gene selection substantially. The experimental results also indicate that the proposed methods are very robust under all the investigated cases.