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Multi-objective Feature Selection with NSGA II

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Adaptive and Natural Computing Algorithms (ICANNGA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4431))

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Abstract

This paper deals with the multi-objective definition of the feature selection problem for different pattern recognition domains. We use NSGA II the latest multi-objective algorithm developed for resolving problems of multi-objective aspects with more accuracy and a high convergence speed. We define the feature selection as a problem including two competing objectives and we try to find a set of optimal solutions so called Pareto-optimal solutions instead of a single optimal solution. The two competing objectives are the minimization of both the number of used features and the classification error using 1-NN classifier. We apply our method to five databases selected from the UCI repository and we report the results on these databases. We present the convergence of the NSGA II on different problems and discuss the behavior of NSGA II on these different contexts.

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Authors and Affiliations

  1. REsearch Group on Intelligent Machines (REGIM), University of Sfax, ENIS, BP. W-3038 – Sfax, Tunisia

    Tarek M. Hamdani & Adel M. Alimi

  2. Pattern Analysis and Machine Intelligence Research Group, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

    Jin-Myung Won & Fakhri Karray

Authors
  1. Tarek M. Hamdani
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  2. Jin-Myung Won
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  3. Adel M. Alimi
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  4. Fakhri Karray
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Editor information

Bartlomiej Beliczynski Andrzej Dzielinski Marcin Iwanowski Bernardete Ribeiro

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© 2007 Springer Berlin Heidelberg

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Hamdani, T.M., Won, JM., Alimi, A.M., Karray, F. (2007). Multi-objective Feature Selection with NSGA II. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_27

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  • DOI: https://doi.org/10.1007/978-3-540-71618-1_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71589-4

  • Online ISBN: 978-3-540-71618-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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