The nature of statistical learning theory
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A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Identifying mobile phone design features critical to user satisfaction
Human Factors in Ergonomics & Manufacturing
A neurofuzzy-evolutionary approach for product design
Integrated Computer-Aided Engineering
Engineering Applications of Artificial Intelligence
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
Classification model for product form design using fuzzy support vector machines
Computers and Industrial Engineering
A support vector regression based prediction model of affective responses for product form design
Computers and Industrial Engineering
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
How will the use of graphics affect visual aesthetics? A user-centered approach for web page design
International Journal of Human-Computer Studies
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This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.