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Variable Precision Bayesian Rough Set Model and Its Application to Kansei Engineering

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Transactions on Rough Sets V

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4100))

Abstract

This paper proposes a rough set method to extract decision rules from human evaluation data with much ambiguity such as sense and feeling. To handle totally ambiguous and probabilistic human evaluation data, we propose an extended decision table and a probabilistic set approximation based on a new definition of information gain. Furthermore, for our application, we propose a two-stage method to extract probabilistic if-then rules simply using decision functions of approximate regions. Finally, we implemented the computer program of our proposed rough set method and applied it to Kansei Engineering of coffee taste design and examined the effectiveness of the proposed method. The result shows that our proposed rough set method is definitely applicable to human evaluation data.

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Author information

Authors and Affiliations

  1. Department of Kansei Information, Faculty of Human and Social Environments, Hiroshima International University, 555-36 Kurose, Higashihiroshima, Hiroshima, 724-0695, Japan

    Tatsuo Nishino, Mitsuo Nagamachi & Hideo Tanaka

Authors
  1. Tatsuo Nishino
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  2. Mitsuo Nagamachi
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  3. Hideo Tanaka
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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Nishino, T., Nagamachi, M., Tanaka, H. (2006). Variable Precision Bayesian Rough Set Model and Its Application to Kansei Engineering. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets V. Lecture Notes in Computer Science, vol 4100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11847465_9

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  • DOI: https://doi.org/10.1007/11847465_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-39382-5

  • Online ISBN: 978-3-540-39383-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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