Ant feature selection using fuzzy decision functions

S.M. Vieira, J.M. Costa Sousa, da, U. Kaymak

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

1 Citation (Scopus)


One of the most important stages in data preprocessing for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease in general the classification accuracy, and enlarge the complexity of the classifier. Feature selection is a multi-criteria optimization problem with contradictory objectives, which are difficult to properly describe by conventional cost functions. This chapter proposes the use of fuzzy optimization to improve the performance of this type of system, since it allows for an easier and more transparent description of the criteria used in the feature selection process. In our previous work, an ant colony optimization algorithm for feature selection was proposed, which minimized two objectives: number of features and classification error. In this chapter, a fuzzy objective function is proposed to cope with the difficulty of weighting the different criteria involved in the optimization algorithm. The application of fuzzy feature selection to two benchmark problems show the usefulness of the proposed approach.
Original languageEnglish
Title of host publicationFuzzy optimization : recent advances and applications
EditorsW.A. Lodwick, J. Kacprzyk
Place of PublicationBerlin
Number of pages533
ISBN (Print)978-3-642-13934-5
Publication statusPublished - 2010

Publication series

NameStudies in fuzziness and soft computing
ISSN (Print)1434-9922


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