A two-stage method to select a smaller subset of informative genes for cancer classification

Gene expression data measured by microarray machines are useful for cancer classification. However, it faces with several problems in selecting genes for the classification due to many irrelevant genes, noisy data, and the availability of a small number of samples compared to a huge number of genes (high-dimensional data). Hence, this paper proposes a two-stage gene selection method to select a smaller (near-optimal) subset of informative genes that is most relevant for the cancer classification. It has two stages: 1) pre-selecting genes using a filter method to produce a subset of genes; 2) optimising the gene subset using a multi-objective hybrid method to automatically yield a smaller subset of informative genes. Three gene expression data sets are used to test the effectiveness of the proposed method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works.