NON-LINEAR DATA ANALYSIS ON KANSEI ENGINEERING AND DESIGN EVALUATION BY GENETIC ALGORITHM

Abstract

This paper discusses non-linearity in KANSEI evaluation data and attempts to better understand non-linear relationships between design attributes. Conventionally, the basis of KANSEI analysis has been multivariate analyses and linear modeling. These methods have proven unreliable and problematic. Non-linearity may be typically represented by the interaction between design elements, which often is represented as multicollinearity. To better understand such interactions, a method is proposed that expresses the effect of combining multiple design elements, and uses 'f-then' rules in conjunction with decision trees. The method is also described for constructing these decision trees by using genetic algorithms (GA). Our concern is to consider the adequateness of the proposed method for analyses of non-linear KANSEI data. Two actual examples are shown in order to investigate this problem. An example of KANSEI experiment on interior automobile space is used to examine divided partitions as tree representations. Another illustration of canned coffee design shows a comparison between the results of our method and quantification type I. We can conclude from these examples such that the proposed method indicates in more detailed rules and a deeper understanding of information extracted from KANSEI evaluation data.