抄録
The recent development of microarray technologies has enabled biologists to quantify gene expression of thousands of genes in a single experiment. Microarray data are expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. The four heterogeneous childhood cancers, namely neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT) and thus often lead to misdiagnosis. The selection of informative genes for classifying these cancers is a main problem. 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 optimization to select a near-optimal (smaller) subset of informative genes that is relevant for cancer classification. The proposed method introduces a modified rule for the position update. It is applied on the SRBCT microarray data set. Experimental results show that our proposed method is superior to the standard version of binary particle swarm optimization and other related previous works.
