An improved binary particle swarm optimisation for gene selection in classifying cancer classes

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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 smaller subset of genes from the thousands of genes in the data that contributes to a disease. 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 optimisation to select a near-optimal (smaller) subset of informative genes that is relevant for cancer classification. Experimental results show that the performance of the proposed method is superior to a standard version of particle swarm optimisation and other related previous works in terms of classification accuracy and the number of selected genes.

Original languageEnglish
Title of host publicationDistributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, Ambient Assisted Living - 10th Int. Work-Conf. Artificial Neural Networks, IWANN 2009 Workshops, Proceedings
Pages495-502
Number of pages8
EditionPART 2
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain
Duration: Jun 10 2009Jun 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5518 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Work-Conference on Artificial Neural Networks, IWANN 2009
Country/TerritorySpain
CitySalamanca
Period6/10/096/12/09

Keywords

  • Gene selection
  • Hybrid approach
  • Microarray data
  • Particle swarm optimisation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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