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Fuzzy Multi-Sphere Support Vector Data Description

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Advances in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7819))

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Abstract

Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.

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Author information

Authors and Affiliations

  1. HCM City University of Education, Vietnam

    Trung Le

  2. University of Canberra, ACT, 2601, Australia

    Dat Tran & Wanli Ma

Authors
  1. Trung Le
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  2. Dat Tran
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  3. Wanli Ma
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Editor information

Editors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  2. Dept. of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao  & Guandong Xu  & 

  4. Asian Office of Aerospace Research and Development (AOARD), Air Force Office of Scientific Research (AFOSR), Air Force Research Laboratory USA, Osaka University, 7-23-17 Roppongi, 106-0032, Minato-ku, Tokyo, Japan

    Hiroshi Motoda

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Le, T., Tran, D., Ma, W. (2013). Fuzzy Multi-Sphere Support Vector Data Description. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_48

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  • DOI: https://doi.org/10.1007/978-3-642-37456-2_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37455-5

  • Online ISBN: 978-3-642-37456-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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