Advances in neural information processing systems 2
Pairwise classification and support vector machines
Advances in kernel methods
Variable selection using svm based criteria
The Journal of Machine Learning Research
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
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
Computers and Industrial Engineering
Hi-index | 12.05 |
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.