@ARTICLE { Rsds4387,
author = {Tanaka, Hideo and Nagamachi, Mitsuo and Nishino, Tatsuo}, 
title = {Variable Precision Bayesian Rough Set Model and Its Application to Kansei Engineering}, 
booktitle = {T. Rough Sets}, 
journal = {Transactions on Rough Sets}, 
number = 	{(no. V)}, 
volume = 4100, 
pages = {190-206}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2006, 
issn = {0302-9743}, 
isbn = {978-3-540-39382-5}, 
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.}, 
keywords = {variable precision bayesian rough set model, ambiguity, extraction of decision rules, human evaluation data, kansei engineering, rough sets, }, 
}