An enhancement of binary particle swarm optimization based on the proposed constraint and rule for selecting a small subset of informative genes

In order to select a small subset of informative genes from the gene expression data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose enhanced binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce the constraint of elements of particle velocity vectors, and we propose a rule for updating particle?s positions. By performing experiments on five different gene expression data sets, we have found that the performance of the proposed method is superior to the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.