Artificial Life and Robotics

, Volume 14, Issue 1, pp 16–19

Particle swarm optimization for gene selection in classifying cancer classes

Original Article

DOI: 10.1007/s10015-009-0712-z

Cite this article as:
Mohamad, M.S., Omatu, S., Deris, S. et al. Artif Life Robotics (2009) 14: 16. doi:10.1007/s10015-009-0712-z


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.

Key words

Gene selectionHybrid approachMicroarray dataParticle swarm optimization

Copyright information

© International Symposium on Artificial Life and Robotics (ISAROB). 2009

Authors and Affiliations

  1. 1.Department of Computer Science and Intelligent Systems, Graduate School of EngineeringOsaka Prefecture UniversitySakai, OsakaJapan
  2. 2.Department of Software Engineering, Faculty of Computer Science and Information SystemsUniversiti Teknologi MalaysiaSkudai, JohoreMalaysia