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Data Cleaning Using Complementary Fuzzy Support Vector Machine Technique

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

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Abstract

In this paper, a Complementary Fuzzy Support Vector Machine (CMTFSVM) technique is proposed to handle outlier and noise in classification problems. Fuzzy membership values are applied for each input point to reflect the degree of importance of the instances. Datasets from the UCI and KEEL are used for the comparison. In order to confirm the proposed methodology, 40 % random noise is added to the datasets. The experiment results of CMTFSVM are analysed and compared with the Complementary Neural Network (CMTNN). The outcome indicated that the combined CMTFSVM outperformed the CMTNN approach.

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Authors and Affiliations

  1. School of Engineering and Information Technology, Murdoch University, Perth, Australia

    Ratchakoon Pruengkarn, Kok Wai Wong & Chun Che Fung

Authors
  1. Ratchakoon Pruengkarn
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  2. Kok Wai Wong
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  3. Chun Che Fung
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Corresponding author

Correspondence to Ratchakoon Pruengkarn .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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Pruengkarn, R., Wong, K.W., Fung, C.C. (2016). Data Cleaning Using Complementary Fuzzy Support Vector Machine Technique. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_19

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  • DOI: https://doi.org/10.1007/978-3-319-46672-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

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