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Soft Computing

, Volume 12, Issue 2, pp 111–120 | Cite as

A genetic algorithm-based method for feature subset selection

  • Feng TanEmail author
  • Xuezheng Fu
  • Yanqing Zhang
  • Anu G. Bourgeois
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Abstract

As a commonly used technique in data preprocessing, feature selection selects a subset of informative attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the comprehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria has been introduced by researchers. However, it is discovered that no single criterion is best for all applications. In this paper, we propose a framework based on a genetic algorithm (GA) for feature subset selection that combines various existing feature selection methods. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. We conducted experiments using three data sets and three existing feature selection methods. The experimental results demonstrate that our approach is a robust and effective approach to find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm.

Keywords

Feature selection Gene Selection Genetic algorithm Microarray gene expression data analysis 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Feng Tan
    • 1
    Email author
  • Xuezheng Fu
    • 1
  • Yanqing Zhang
    • 1
  • Anu G. Bourgeois
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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