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Modelling data by the Choquet integral

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Information Fusion in Data Mining

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 123))

Abstract

The chapter makes a survey of works dealing with the Choquet integral as a general non linear regression model. It is shown that its use is however limited to commensurate variables, as it is the case for example for multicriteria evaluation and multiattribute classification. A large part is devoted to the various methods of identifying parameters of the model, essentially quadratic programming and genetic algorithms. A new approach based on genetic algorithms is also described. Lastly, related works on classification and subjective evaluation are mentionned.

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Author information

Authors and Affiliations

  1. LIP6, Université de Paris VI, Paris, France

    Michel Grabisch

  2. THALES, Corporate Research Laboratory, Domaine de Corbeville, Orsay, France

    Michel Grabisch

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  1. Michel Grabisch
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Editor information

Editors and Affiliations

  1. Institut d’Investigació en Intel·ligència Artificial CSIC - Spanish Scientific Research Council, Campus Universitat Autònoma de Barcelona, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

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© 2003 Springer-Verlag Berlin Heidelberg

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Grabisch, M. (2003). Modelling data by the Choquet integral. In: Torra, V. (eds) Information Fusion in Data Mining. Studies in Fuzziness and Soft Computing, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36519-8_8

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  • DOI: https://doi.org/10.1007/978-3-540-36519-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05628-4

  • Online ISBN: 978-3-540-36519-8

  • eBook Packages: Springer Book Archive

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