Variable precision bayesian rough set model and its application to human evaluation data

  • Authors:

  • Tatsuo Nishino;Mitsuo Nagamachi;Hideo Tanaka

  • Affiliations:

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

  • Venue:
  • RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
  • Year:
  • 2005

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Abstract

This paper focuses on a rough set method to analyze human evaluation data with much ambiguity such as sensory and feeling data. In order to handle totally ambiguous and probabilistic human evaluation data, we propose a probabilistic approximation based on information gains of equivalent classes. Furthermore, we propose a two-stage method to simply extract uncertain if–then rules using decision functions of approximate regions. Finally, we applied the proposed method to practical human sensory evaluation data and examined the effectiveness of the proposed method. The result shown that our proposed rough set method is more applicable to human evaluation data.