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Comparison of Multi-objective Algorithms Applied to Feature Selection

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Towards Advanced Data Analysis by Combining Soft Computing and Statistics

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 285))

Abstract

The feature selection problem can be formulated as a multi-objective optimization (MOO) problem, as it involves the minimization of the feature subset cardinality and the misclassification error. In this chapter, a comparison of MOO algorithms applied to feature selection is presented. The used MOO methods are: Nondominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi Objective Simulated Annealing (AMOSA), and Direct Multi Search (DMS). To test the feature subset solutions, Takagi- Sugeno fuzzy models are used as classifiers. To solve the feature selection problem, AMOSA was adapted to deal with discrete optimization. The multi-objective methods are applied to four benchmark datasets used in the literature and the obtained results are compared and discussed.

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Author information

Authors and Affiliations

  1. Faculty of Science, Statistics Department, Ankara University, 06100, Ankara, Turkey

    Özlem Türkşen & Ayşen Apaydin

  2. Instituto Superior Técnico, Dept. of Mechanical Engineering, CIS - IDMEC/LAETA, Technical University of Lisbon, Lisbon, Portugal

    Susana M. Vieira, José F. A. Madeira & João M. C. Sousa

  3. ISEL, Rua Conselheiro Emidio Navarro, 1, 1959-007, Lisboa, Portugal

    José F. A. Madeira

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  1. Özlem Türkşen
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  2. Susana M. Vieira
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  3. José F. A. Madeira
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  4. Ayşen Apaydin
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  5. João M. C. Sousa
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Correspondence to Özlem Türkşen .

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Editors and Affiliations

  1. Intelligent Data Analysis & Graphical, Models Research Unit, European Centre for Soft Computing, C/ Gonzalo Gutierre, Mieres, 33600, Asturias, Spain

    Christian Borgelt

  2. , Departamento de Estadistica e I. O. y, Universidad de Oviedo, C/Calvo Sotelo, s/n, Oviedo, 33071, Spain

    María Ángeles Gil

  3. Instituto Superior Técnico, Department of Mechanical Engineering, Technical University Lisbon, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal

    João M.C. Sousa

  4. Labo. Microelectronique, Université Catholique de Louvain, place du Levant 3, Leuven, 1348, Belgium

    Michel Verleysen

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Türkşen, Ö., Vieira, S.M., Madeira, J.F.A., Apaydin, A., Sousa, J.M.C. (2013). Comparison of Multi-objective Algorithms Applied to Feature Selection. In: Borgelt, C., Gil, M., Sousa, J., Verleysen, M. (eds) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30278-7_28

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  • DOI: https://doi.org/10.1007/978-3-642-30278-7_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30277-0

  • Online ISBN: 978-3-642-30278-7

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