Efficient gene selection for classification of microarray data

Shinn-Ying Ho*, Chong Cheng Lee, Hung Ming Chen, Hui Ling Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Microarray is a useful technique for measuring expression data of thousands of genes simultaneously. One of challenges in classification of microarray data is to select a minimal number of relevant genes which can maximize classification accuracy. Many gene selection methods as well as their corresponding classifiers have been proposed. One of existing analysis methods is the hybrid approach based on genetic algorithm and maximum likelihood classification (GA/MLHD). In this paper, an intelligent genetic algorithm (IGA) using control genes and an improved fitness function is proposed to determine the minimal number of relevant genes and identify these genes, while maximizing classification accuracy simultaneously. The experimental results show that our approach is superior to the existing method GA/MLHD in terms of the number of selected genes, classification accuracy, and robustness of selected genes and accuracy, especially for the datasets which have numerous categories and a large number of testing genes inside.

Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages1753-1760
Number of pages8
DOIs
StatePublished - 31 Oct 2005
Event2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom
Duration: 2 Sep 20055 Sep 2005

Publication series

Name2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Volume2

Conference

Conference2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
CountryUnited Kingdom
CityEdinburgh, Scotland
Period2/09/055/09/05

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