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Multi-granular Evaluation Model Through Fuzzy Random Regression to Improve Information Granularity

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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

Extracting new information through regression analysis is somewhat difficult in environment which contains fuzzy and random situation; shows simultaneous uncertainty. Given this coexistence of random and fuzzy information, the data cannot be adequately treated by a conventional regression method. Thus, in this paper, a fuzzy random regression is introduced to improve the extraction of weight of granules in a multi-granular decision making. The proposed model will manage the multi-granular linguistic labels provided by evaluators in order to compute collective assessments about the product samples that will be used by the decision maker to determine final decision. The proposed model is applied to oil palm fruit grading, as the quality inspection process for fruits requires a method to ensure product quality. We include simulation results and highlight the advantage of the proposed method in handling the existence of fuzzy random information.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400, Johor, Malaysia

    Nureize Arbaiy

  2. Graduate School of Information, Production and System, Waseda University, 2-7 Hibikino, Wakamatsu, Kitakyushu, 808-0135, Japan

    Junzo Watada

Authors
  1. Nureize Arbaiy
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  2. Junzo Watada
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Corresponding author

Correspondence to Junzo Watada .

Editor information

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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Arbaiy, N., Watada, J. (2015). Multi-granular Evaluation Model Through Fuzzy Random Regression to Improve Information Granularity. 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_11

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  • DOI: https://doi.org/10.1007/978-3-319-08254-7_11

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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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