Noise-based feature perturbation as a selection method for microarray data

Li Chen, Dmitry B. Goldgof, Lawrence O. Hall, Steven A. Eschrich

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

Abstract

DNA microarrays can monitor the expression levels of thousands of genes simultaneously, providing the opportunity for the identification of genes that are differentially expressed across different conditions. Microarray datasets are generally limited to a small number of samples with a large number of gene expressions, therefore feature selection becomes a very important aspect of the microarray classification problem. In this paper, a new feature selection method, feature perturbation by adding noise, is proposed to improve the performance of classification. The experimental results on a benchmark colon cancer dataset indicate that the proposed method can result in more accurate class predictions using a smaller set of features when compared to the SVM-RFE feature selection method.

Original languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - Third International Symposium, ISBRA 2007, Proceedings
PublisherSpringer Verlag
Pages237-247
Number of pages11
ISBN (Print)3540720308, 9783540720300
DOIs
StatePublished - 2007
Event3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007 - Atlanta, GA, United States
Duration: May 7 2007May 10 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4463 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Symposium Bioinformatics Research and Applications, ISBRA 2007
CountryUnited States
CityAtlanta, GA
Period5/7/075/10/07

Keywords

  • Classification
  • Feature perturbation
  • Gene selection
  • Microarray gene expression data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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