Paper Info
Title
A classification-based Kansei engineering system for modeling consumers' affective responses and analyzing product form features
Abstract
In the product design field, modeling consumers' affective responses (CARs) for product form design is very helpful for developing successful products. It is also important for product designers to identify critical product form features (PFFs) to aid them in producing appealing products. In the present paper, a classification-based Kansei engineering system (KES) is proposed for modeling CARs and analyzing PFFs in a systematic manner. First, single adjectives are collected as initial affective dimensions for consumers to evaluate a set of representative products in the first questionnaire experiment. Factor analysis (FA) combined with Procrustes analysis (PA) is then used to extract representative affective dimensions. Second, these representative adjectives are regarded as class labels for consumers to describe their affective responses toward product form design. A large set of product samples are analyzed and their PFFs are encoded into numerical format. In the second questionnaire experiment, consumers are asked to assign one most suitable class labels to each product samples. A multiclass support vector machine (SVM) classification model is constructed for relating CARs and the PFFs. Optimal training parameters of SVM can be determined by a two-step cross-validation (CV). Third, support vector machine recursive feature elimination (SVM-RFE) is applied to pin point critical PFFs by wither using overall ranking or class-specific ranking. The relative importance of each PFF can be also analyzed by examining the weight distribution of the PFFs in each elimination step. A case study of digital camera design is also given to demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2011
10.1016/j.eswa.2011.03.008
Expert Syst. Appl.
Keywords
Field
DocType
factor analysis (fa),product form design,classification-based kansei engineering system,product design field,representative product,product designer,questionnaire experiment,support vector machine recursive feature elimination (svm-rfe),successful product,product sample,affective response,critical product form feature,procrustes analysis (pa),kansei engineering,appealing product,support vector machine (svm),weight distribution,factor analysis,product design,support vector machine,cross validation
Data mining,Ranking,Computer science,Support vector machine,Kansei engineering,Procrustes analysis,Digital camera,Artificial intelligence,Weight distribution,Product design,Machine learning,Recursion
Journal
Volume
Issue
ISSN
38
9
Expert Systems With Applications
Citations 
PageRank 
References 
3
0.45
8
Authors
1
Name
Order
Citations
PageRank
Chih-Chieh Yang112713.88