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Gaussian Fuzzy Integral Based Classification

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 5))

Abstract

Fuzzy integral is a kind of effective fusion tool. Traditionally, fuzzy integral can project the data with n-dimension into one line, in which the projection is along with a group of linear lines. In reality, data distribution is not regular, so the straight line for projection is too limited. Gaussian function is applied to natural science widely. It is close to normal distribution and can cover more data. In this article, a new generalization of fuzzy integral is proposed. The Gaussian function is used as integrand. A new classifier is constructed based on Gaussian Fuzzy integral and applied into several benchmark data sets. The results show that the new version can improve the property of fuzzy integral and obtain the better performance.

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Author information

Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Wang Jinfeng

  2. College of Economics and Management, South China Agricultural University, Guangzhou, 510642, China

    Wang Wenzhong

Authors
  1. Wang Jinfeng
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  2. Wang Wenzhong
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Corresponding author

Correspondence to Wang Wenzhong .

Editor information

Editors and Affiliations

  1. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Kittichai Lavangnananda

  2. School of Computing and Informatics, Institut Teknologi Brunei, Gadong, Brunei Darussalam

    Somnuk Phon-Amnuaisuk

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Worrawat Engchuan

  4. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

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© 2016 Springer International Publishing Switzerland

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Jinfeng, W., Wenzhong, W. (2016). Gaussian Fuzzy Integral Based Classification. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-27000-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26999-3

  • Online ISBN: 978-3-319-27000-5

  • eBook Packages: EngineeringEngineering (R0)

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