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Building Fuzzy Robust Regression Model Based on Granularity and Possibility Distribution

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Information Granularity, Big Data, and Computational Intelligence

Part of the book series: Studies in Big Data ((SBD,volume 8))

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Abstract

The characteristic of the fuzzy regression model is to enwrap all the given samples. The fuzzy regression model enables us to take the possibility interval for a granular instead of a single numerical value. This granular provides the wider treatment for us to human-centered understanding of the latent system. Such a granule or interval of fuzzy regression model is created by considering how far a sample is from the central values. That means when samples are widely scattered the size of a granular or an interval of the fuzzy model is widened. That is, the fuzziness of the fuzzy regression model is decided by the range of sample distribution. Therefore, outliers make the fuzzy regression model distorted. This chapter describes the model building of fuzzy robust regression from the perspective of granularity by removing improper data based on genetic algorithm. Moreover, let us build the fuzzy regression model that places the largest grade on the central point of scattering samples.

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Authors and Affiliations

  1. Faculty of Economics, Shimonoseki City University, 2-1-1 Daigaku-Cho, Shimonoseki, Yamaguchi, 751-8510, Japan

    Yoshiyuki Yabuuchi

  2. Graduate School of Information, Production and Systems, Waseda University, 2-4 Hibikino Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan

    Junzo Watada

Authors
  1. Yoshiyuki Yabuuchi
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  2. Junzo Watada
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Correspondence to Junzo Watada .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Yabuuchi, Y., Watada, J. (2015). Building Fuzzy Robust Regression Model Based on Granularity and Possibility Distribution. In: Pedrycz, W., Chen, SM. (eds) Information Granularity, Big Data, and Computational Intelligence. Studies in Big Data, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-08254-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-08254-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08253-0

  • Online ISBN: 978-3-319-08254-7

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