Title :
A method for learning decision tree using genetic algorithm and its application to Kansei engineering system
Author :
Tsuchiya, T. ; Ishihara, S. ; Matsubara, Y. ; Nishino, Takanori ; Nagamach, M.
Author_Institution :
Shimonoseki City Univ., Japan
Abstract :
This paper shows the method for learning kansei reasoning rules and its application to canned coffee design. We first obtained the Kansei evaluation data by experiment with canned coffee. Then we analyzed the data by using genetic algorithm. This method extracts Kansei rules to represent the relationship between design elements of canned coffee and Kansei. Extracted rules are compared with results of the conventional statistical method. The results of the method express the design images on the combination design of canned coffee. We show that the acquired rules represent the relationship between human image and the combination of design elements. From the experimental results, the learning method is able to extract the nonlinear relationship among the design elements
Keywords :
decision trees; food processing industry; genetic algorithms; human factors; inference mechanisms; knowledge based systems; learning (artificial intelligence); product development; CAD; Kansei engineering; canned coffee; decision tree; genetic algorithm; learning; product design; reasoning rules; Decision trees; Design engineering; Engines; Genetic algorithms; Genetic engineering; Humans; Image analysis; Image databases; Learning systems; Product design;
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
0-7803-5731-0
DOI :
10.1109/ICSMC.1999.816564
