Classification model for product form design using fuzzy support vector machines

Consumer preferences regarding product design are often affected by a large variety of form features. Traditionally, the quality of product form design depended heavily on designers' intuitions and did not always prove to be successful in the marketplace. In this study, to help product designers develop appealing products in a more effective manner, an approach based on fuzzy support vector machines (fuzzy SVM) is proposed. This constructs a classification model of product form design based on consumer preferences. The one-versus-one (OVO) method is adopted to handle a multiclass problem by breaking it into various two-class problems. Product samples were collected and their form features were systematically examined. To formulate a classification problem, each product sample was assigned a class label and a fuzzy membership that corresponded to this label. The OVO fuzzy SVM model was constructed using collected product samples. The optimal training parameter set for the model was determined by a two-step cross-validation. A case study of mobile phone design is given to demonstrate the effectiveness of the proposed methodology. The performance of fuzzy SVM is also compared with SVM. The results of the experiment show that fuzzy SVM performed better than SVM.

[1]  ShiehMeng-Dar,et al.  Multiclass SVM-RFE for product form feature selection , 2008 .

[2]  Victor L. Brailovsky,et al.  On domain knowledge and feature selection using a support vector machine , 1999, Pattern Recognit. Lett..

[3]  Sung H. Han,et al.  A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design , 2004 .

[4]  Shang H. Hsu,et al.  A semantic differential study of designers’ and users’ product form perception , 2000 .

[5]  Guido Smits,et al.  Improved SVM regression using mixtures of kernels , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[6]  Isabelle Guyon,et al.  Statistical Learning and Kernel Methods in Bioinformatics , 2003 .

[7]  Myung Hwan Yun,et al.  Identifying mobile phone design features critical to user satisfaction , 2004 .

[8]  S. Sathiya Keerthi,et al.  Which Is the Best Multiclass SVM Method? An Empirical Study , 2005, Multiple Classifier Systems.

[9]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[10]  Isabelle Guyon,et al.  Comparison of classifier methods: a case study in handwritten digit recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[11]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[14]  Werner Dubitzky,et al.  Erratum: Avoiding model selection bias in small-sample genomic datasets (Bioinformatics (2006) vol. 22 (10) (1245-1250)) , 2006 .

[15]  Nello Cristianini,et al.  Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[16]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[17]  Werner Dubitzky,et al.  Avoiding model selection bias in small-sample genomic datasets , 2006, Bioinform..

[18]  C. J. C. Burges.  Handwritten digit string recognition , 1998 .

[19]  Tomio Jindo,et al.  A fuzzy logic analysis method for evaluating human sensitivities , 1995 .

[20]  H. P. Huang,et al.  Fuzzy Support Vector Machines for Pattern Recognition and Data Mining , 2002 .

[21]  Jiyoung Kwahk,et al.  A methodology for evaluating the usability of audiovisual consumer electronic products. , 2002, Applied ergonomics.

[22]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[23]  한성호,et al.  Identifying mobile phone design features critical to user satisfaction , 2001 .

[24]  Mitsuo Nagamachi,et al.  Development of a design support system for office chairs using 3-D graphics , 1995 .

[26]  V. Rao,et al.  Research for product positioning and design decisions: An integrative review , 1995 .

[27]  Wenjian Wang,et al.  Determination of the spread parameter in the Gaussian kernel for classification and regression , 2003, Neurocomputing.

[28]  Chih-Chieh Yang,et al.  Multiclass SVM-RFE for product form feature selection , 2008, Expert Syst. Appl..