title: An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes.
abstract: BACKGROUND
  Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes.
  
  
  METHODS
  We propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle's position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets.
  
  
  RESULTS
  The performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO.
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, Johor, 81310, Malaysia. saberi@utm.my.
authors.forename: Mohd Saberi
authors.initials: MS
authors.lastname: Mohamad
authors.forename: Sigeru
authors.initials: S
authors.lastname: Omatu
authors.forename: Safaai
authors.initials: S
authors.lastname: Deris
authors.forename: Michifumi
authors.initials: M
authors.lastname: Yoshioka
authors.forename: Afnizanfaizal
authors.initials: A
authors.lastname: Abdullah
authors.forename: Zuwairie
authors.initials: Z
authors.lastname: Ibrahim