An improved binary particle swarm optimisation for gene selection in classifying cancer classes

• Main Authors: Mohamad, Mohd. Saberi, Zainal, Anazida, Deris, Safa'ai
• Format: Book Section
• Published: Springer 2009
• Subjects: QA75 Electronic computers. Computer science; Unspecified
• Online Access: http://eprints.utm.my/id/eprint/14441/
• Institution: Universiti Teknologi Malaysia

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 smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult because of the availability of the small number of samples compared to the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this paper proposes an improved binary particle swarm optimisation to select a near-optimal (smaller) subset of informative genes that is relevant for cancer classification. Experimental results show that the performance of the proposed method is superior to a standard version of particle swarm optimisation and other related previous works in terms of classification accuracy and the number of selected genes.