Variable precision Bayesian rough set model and its application to Kansei engineering

  • Authors:

  • Tatsuo Nishino;Mitsuo Nagamachi;Hideo Tanaka

  • Affiliations:

  • Department of Kansei Information, Faculty of Human and Social Environments, Hiroshima International University, Higashihiroshima, Hiroshima, Japan;Department of Kansei Information, Faculty of Human and Social Environments, Hiroshima International University, Higashihiroshima, Hiroshima, Japan;Department of Kansei Information, Faculty of Human and Social Environments, Hiroshima International University, Higashihiroshima, Hiroshima, Japan

  • Venue:
  • Transactions on Rough Sets V
  • Year:
  • 2006

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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.