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Meta-classifiers and Selective Superiority

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Intelligent Problem Solving. Methodologies and Approaches (IEA/AIE 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1821))

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Abstract

Given that no one classification method is the best in all tasks, a variety of approaches have evolved to prevent poor performance due to mismatch of capabilities. One approach to overcome this problem is to determine when a method may be appropriate for a given problem. A second, more popular approach is to combine the capabilities of two or more classification methods. This paper provides some evidence that the combining of classifiers can yield more robust solutions.

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Author information

Authors and Affiliations

  1. Center for Advanced Computer Studies, University of Louisiana, Lafayette, LA, 70504

    Ryan Benton, Miroslav Kubat & Rasaiah Loganantharaj

Authors
  1. Ryan Benton
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  2. Miroslav Kubat
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  3. Rasaiah Loganantharaj
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Editor information

Editors and Affiliations

  1. The Center for Advanced Computer Studies, University of Lousiana, 2 Rex Street, Lafayette, LA, 70504-4330, USA

    Rasiah Logananthara

  2. Department of Neural Information Processing, University of Ulm, Oberer Eselsberg, 89069, Ulm, Germany

    Günther Palm

  3. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666-4616, USA

    Moonis Ali

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© 2000 Springer-Verlag Berlin Heidelberg

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Benton, R., Kubat, M., Loganantharaj, R. (2000). Meta-classifiers and Selective Superiority. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_53

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  • DOI: https://doi.org/10.1007/3-540-45049-1_53

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67689-8

  • Online ISBN: 978-3-540-45049-8

  • eBook Packages: Springer Book Archive

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