Genetic algorithms and silhouette measures applied to microarray data classification

Microarray technology allows large-scale parallel measurements of the expression of many thousands genes and thus aiding in the development of efficient cancer diagnosis and classification platforms. In this paper, we apply the genetic algorithm and the silhouette statistic in conjunction with several distance functions to the problem of multi-class prediction. We examine two widely used sets of gene expression data, measured across sets of tumors, and present the results of classification accuracy on these two datasets by our methods. Our best success rate of tumor classification has better accuracy than many previously reported methods and it provides a useful method towards a complete tool in this domain.