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Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering

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Fuzzy Logic and Applications (WILF 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8256))

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Abstract

We propose an algorithm for inferring membership functions of fuzzy sets by exploiting a procedure originated in the realm of support vector clustering. The available data set consists of points associated with a quantitative evaluation of their membership degree to a fuzzy set. The data are clustered in order to form a core gathering all points definitely belonging to the set. This core is subsequently refined into a membership function. The method is analyzed and applied to several real-world data sets.

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Author information

Authors and Affiliations

  1. Università degli Studi di Milano, Italy

    Dario Malchiodi

  2. University of Alberta, Canada

    Witold Pedrycz

Authors
  1. Dario Malchiodi
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  2. Witold Pedrycz
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Editor information

Editors and Affiliations

  1. DIBRIS, University of Genoa, Via Dodecaneso 35, 16146, Genoa, Italy

    Francesco Masulli

  2. Dept. of Informatics, Systems, and Communication, University of Milano Bicocca, Viale Sarca 336, 20126, Milan, Italy

    Gabriella Pasi

  3. Dept. of Information Systems, Iona College, 710 North Ave, 10801, New Rochelle, NY, USA

    Ronald Yager

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© 2013 Springer International Publishing Switzerland

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Malchiodi, D., Pedrycz, W. (2013). Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering. In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-03200-9_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03199-6

  • Online ISBN: 978-3-319-03200-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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