A Consideration of Fuzzy Decision Tree Construction Method Considering Missing Evaluation Items

Abstract

In this paper, we propose a decision tree construction method to complement missing evaluation items from questionnaire items in Kansei analysis method with Fuzzy C4.5 decision tree. When questionnaires to be carried out for impression analysis omit items that users emphasize, Kansei analysis might not be performed appropriately. Therefore, we generate a decision tree using the Fuzzy C4.5 algorithm and estimate the evaluation value of the missing evaluation items from the relation of the information gain ratio which is the feature of the algorithm. Next, we compare the classification abilities of Fuzzy C4.5 decision tree that complement the estimated missing items with standard one through simulation.