Selecting genes from gene expression data by using an enhancement of binary particle swarm optimization for cancer classification

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

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

Abstract

In order to select a small subset of informative genes from 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 an enhancement of binary particle swarm optimization to select a small subset of informative genes that is relevant for classifying cancer samples more accurately. In this proposed method, three approaches have been introduced to increase the probability of bits in particle's positions to be zero. By performing experiments on three different gene expression data sets, we have found that the performance of the proposed method is 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 produces lower running times compared to BPSO.

Original languageEnglish
Title of host publicationICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
Pages82-89
Number of pages8
Publication statusPublished - 2010
Externally publishedYes
Event2nd International Conference on Agents and Artificial Intelligence, ICAART 2010 - Valencia, Spain
Duration: Jan 22 2010Jan 24 2010

Publication series

NameICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
Volume1

Conference

Conference2nd International Conference on Agents and Artificial Intelligence, ICAART 2010
Country/TerritorySpain
CityValencia
Period1/22/101/24/10

Keywords

  • Binary particle swarm optimization
  • Cancer classification
  • Gene expression data
  • Gene selection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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