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Ant Feature Selection Using Fuzzy Decision Functions

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Book cover Fuzzy Optimization

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 254))

Abstract

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.

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Author information

Authors and Affiliations

  1. Technical University of Lisbon, Instituto Superior Técnico Dept. of Mechanical Engineering, Center of Intelligent Systems/IDMEC, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    Susana M. Vieira & João M. C. Sousa

  2. Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, P.O. Box 1738, 3000, DR, Rotterdam, The Netherlands

    Uzay Kaymak

Authors
  1. Susana M. Vieira
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  2. João M. C. Sousa
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  3. Uzay Kaymak
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Editors and Affiliations

  1. Department of Mathemaitical and Statistical Sciences, University of Colorado Denver, PO Box 173364, 80217-3364, Colorado, Denver, USA

    Weldon A. Lodwick

  2. Systems Research Institute Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Vieira, S.M., Sousa, J.M.C., Kaymak, U. (2010). Ant Feature Selection Using Fuzzy Decision Functions. In: Lodwick, W.A., Kacprzyk, J. (eds) Fuzzy Optimization. Studies in Fuzziness and Soft Computing, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13935-2_16

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  • DOI: https://doi.org/10.1007/978-3-642-13935-2_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13934-5

  • Online ISBN: 978-3-642-13935-2

  • eBook Packages: EngineeringEngineering (R0)

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