Particle swarm optimization for gene selection in classifying cancer classes

Mohd Saberi Mohamad, Sigeru Omatu, Safaai Deris, Michifumi Yoshioka

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)16-19
Number of pages4
JournalArtificial Life and Robotics
Issue number1
Publication statusPublished - 2009
Externally publishedYes


  • Gene selection
  • Hybrid approach
  • Microarray data
  • Particles warm optimization

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • Artificial Intelligence


Dive into the research topics of 'Particle swarm optimization for gene selection in classifying cancer classes'. Together they form a unique fingerprint.

Cite this