Product form feature selection for mobile phone design using LS-SVR and ARD

Journal of Convergence Information Technology

Volumn 6, Issue 2, 2011, Pages 138-150

  Yang, Chih Chieh ^a   Shieh, Meng Dar ^b   Chen, Kuang Hsiung ^a   Lin, Pei Ju ^a  

^a SOUTHERN TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Automatic relevance determination; Bayesian inference; Feature selection; Least squares support vector regression

Indexed keywords

AFFECTIVE RESPONSE; AUTOMATIC RELEVANCE DETERMINATION; BAYESIAN INFERENCE; CROSS VALIDATION; EXPERT OPINION; FEATURE RANKING; FEATURE SELECTION; FORM FEATURES; GRID SEARCH; INPUT DATAS; LEAST SQUARES SUPPORT VECTOR REGRESSION; MANUAL INSPECTION; MOBILE PHONE DESIGN; MODEL SELECTION; MODEL-BASED; MORPHOLOGICAL ANALYSIS; MULTIPLE LINEAR REGRESSIONS; OPTIMAL PARAMETER; OUTPUT VALUES; PREDICTION MODEL; PREDICTIVE PERFORMANCE; PRODUCT DESIGNERS; PRODUCT FORMS; SELECTION PROCESS; SEMANTIC DIFFERENTIAL; SMALL DATA;

BAYESIAN NETWORKS; EVOLUTIONARY ALGORITHMS; INFERENCE ENGINES; LINEAR REGRESSION; MATHEMATICAL MODELS; MOBILE PHONES; MOBILE TELECOMMUNICATION SYSTEMS; PRODUCT DESIGN; SEMANTICS; TELECOMMUNICATION EQUIPMENT; TELEPHONE; TELEPHONE SETS;

FEATURE EXTRACTION;

EID: 79952375167     PISSN: 19759320     EISSN: None     Source Type: Journal    
DOI: 10.4156/jcit.vol6.issue2.15     Document Type: Article

References (24)

1. Jindo, T., Hirasago, K. & Nagamachi, M., "Development of a Design Support System for Office Chairs Using 3-D Graphics", International Journal of Industrial Ergonomics, vol. 15, pp. 49-62,1995.
2. Han, S.H. & Hong, S.W., "A Systematic Approach for Coupling User Satisfaction with Product Design", Ergonomics, vol. 46, no. 13/14, pp. 1441-1461, 2003.
3. Han, S.H., Kim, K.J., Yun, M.H., Hong, S.W. & Kim, J., "Identifying Mobile Phone Design Features Critical to User Satisfaction", Human Factors and Ergonomics in Manufacturing, vol. 14, no. 1, pp. 15-29, 2004.
4. Lai, H.H., Lin, Y.C., Yeh, C.H. & Wei, C.H., "User-oriented Design for the Optimal Combination on Product Design", International Journal of Production Economics, vol. 100, pp. 253-267, 2006.
5. Ding, S., Jia, W., Su, C., Liu, X. & Chen, J., "An Improved BP Neural Network Algorithm Based on Factor Analysis", Journal of Convergence Information Technology, vol. 5, no. 4, pp. 103-108, 2010.
6. Zhao, Y.H., Zhong, P. & Wang, K.N., "Application of Least Squares Support Vector Regression Based on Time Series in Prediction of Gas", Journal of Convergence Information Technology, vol.6, no. 1, pp. 243-250, 2011.
7. Han, S.H. & Yang, H., "Screening Important Design Variables for Building a Usability Model", International Journal of Industrial Ergonomics, vol. 33, pp. 159-171, 2004.
8. Blum, A.L. & Langley, P., "Selection of Relevant Features and Examples in Machine Learning", Artificial Intelligence, vol. 97, pp. 245-271, 1997.
9. Breiman, L., Friedman, J., Olshen, R. & Stone, C., CART: Classification and Regression Trees, CBelmont, California, Wadsworth, 1983.
10. Tibshirani, R., "Regression Shrinkage and Selection via the Lasso", Journal of Royal Statistical Society Series B, vol. 58, no. 1, pp. 267-288, 1996.
11. Guyon, I., Weston, J., Barnhill, S. & Vapnik, V., "Gene Selection for Cancer Classification Using Support Vector Machines", Machine Learning, vol. 46, no. 1-3, pp. 389-422, 2002.
12. MacKay, D., "Bayesian Non-linear Modelling for the Prediction Competition", ASHRAE Transactions, vol. 100, pp. 1053-1062, 1994.
13. Viaene, S., Dedene, G. & Derrig, R.A., "Auto Claim Fraud Detection Using Bayesian Learning Neural Networks", Expert Systems with Applications, vol. 29, pp. 653-666, 2005.
14. Grandvalet, Y. & Canu, S., "Adaptive Scaling for Feature Selection in SVMs", In Advances in Neural Information Processing Systems 15, Becker, S., Thrun, S. & Obermayer, K., Editors, pp. 569-576, MIT Press, 2003.
15. Behrens, T.E.J., Berg, H.J., Jbabdi, S., Rushworth, M.F.S. & Woolrich, M.W., "Probabilistic Diffusion Tractography with Multiple Fibre Orientations: What Can We Gain?", NeuroImage, vol. 34, pp. 144-155, 2007.
16. Wang, D. & Lu, W.Z., "Interval Estimation of Urban Ozone Level and Selection of Influential Factors by Employing Automatic Relevance Determination Model", Chemosphere, vol. 62, pp. 1600-1611, 2006.
17. Lopez, G., Batlles, F.J. & Tovar-Pescador, J., "Selection of Input Parameters to Model Direct Solar Irradiance by Using Artificial Neural Networks", Energy, vol. 30, pp. 1675-1684, 2005.
18. Chu, W., Keerthi, S.S., Ong, C.J. & Ghahramani, Z., "Bayesian Support Vector Machines for Feature Ranking and Selection", In Feature Extraction, Foundations and Applications, Guyon, I., Gunn, S., Nikravesh, M. & Zadeh, L., Editors, Springer Verlag, 2006.
19. Shieh, M.D. & Yang, C.C., "Multiclass SVM-RFE for Product Form Feature Selection", Expert Systems with Applications, vol. 35, pp. 531-541, 2008.
20. Vapnik, V.N., The Nature of Statistical Learning Theory, New York, Springer, 1995.
21. Suykens, J.A.K., Gestel, T.V., Brabanter, D., Moor, B.D. & Vandewalle, J., Least Squares Support Vector Machines, Singapore, World Scientific, 2002.
22. Herbrich, R., Learning Kernel Classifier: Theory and Algorithms, Cambridge, MIT Press, 2002.
23. Jones, J.C., Design Methods, New York, Van Nostrand Reinhold, 1992.
24. Shieh, M.D., Wang, T.H. & Yang, C.C., "Affective Response Dimension Selection for Product Design: A Comparison of Cluster Analysis and Procrustes Analysis", International Journal of Digital Content Technology and its Applications, vol. 5, no. 1, pp. 305-318, 2011.