, Volume 14, Issue 1, pp 16-19
Date: 04 Oct 2009

Particle swarm optimization for gene selection in classifying cancer classes

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The application of microarray data for cancer classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contribute to a disease. This selection process is difficult due to the availability of a small number of samples compared with the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this article proposes an improved binary particle swarm optimization to select a near-optimal (small) subset of informative genes that is relevant for the cancer classification. Experimental results show that the performance of the proposed method is superior to the standard version of particle swarm optimization (PSO) and other previous related work in terms of classification accuracy and the number of selected genes.

This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009