Feature selection for modular GA-based classification

Fangming Zhu, Steven Guan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification. In this paper, feature selection is explored with modular GA-based classification. A new feature selection technique, relative importance factor (RIF), is proposed to find less relevant features in the input domain of each class module. By removing these features, it is aimed to reduce the classification error and dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approach. The experiment results show that RIF can be used to find less relevant features and help achieve lower classification error with the feature space dimension reduced.

Original languageEnglish
Pages (from-to)381-393
Number of pages13
JournalApplied Soft Computing
Issue number4
Publication statusPublished - Sept 2004
Externally publishedYes


  • Class decomposition
  • Classification
  • Feature selection
  • Genetic algorithm


Dive into the research topics of 'Feature selection for modular GA-based classification'. Together they form a unique fingerprint.

Cite this