臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：感性工學系統之變數篩選研究
作者：楊智傑
作者(外文)：Chih-Chieh Yang
校院名稱：國立成功大學
系所名稱：工業設計學系碩博士班
指導教授：謝孟達
學位類別：博士
出版日期：2008
主題關鍵詞：感性工學系統；情感反應尺度篩選；變數篩選；產品外型特徵篩選；Variable selection；Product form feature selection；Kansei engineering system；Affective response dimension selection
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:0
本論文針對感性工學系統(Kansei engineering system)之變數篩選問題進行研究，其中包括情感反應尺度篩選(affective response dimension selection)與產品外型特徵篩選(product form feature selection)這兩個議題。首先，情感反應尺度篩選的關鍵點在於：如何挑選合適的形容詞來描述消費者的情感反應？另外一方面，產品外型特徵篩選的關鍵在於：如何找出會顯著影響消費者情感反應的關鍵產品外型特徵？為了要挑選出代表性的形容詞，本研究結合了因素分析(factor analysis)與普魯斯特分析(Procrustes analysis)，挑選出的形容詞可以用於語意差異法(semantic differential method)來收集消費者的情感反應。為了要建立起產品外型特徵與消費者情感反應之間的關聯，可以使用分類(classification)與迴歸(regression)這兩種預測模型。在分類預測模型部分，使用多類別模糊支援向量機(multiclass fuzzy support vector machine)來建立，這樣的模型可以準確地針對輸入的產品外型特徵分辨出不同的消費者情感反應。在迴歸預測模型部分，使用支援向量迴歸(support vector regression)針對輸入的產品外型特徵來預測消費者情感反應。最後，為了能夠在預測模型中挑選關鍵的產品外型特徵，將支援向量機遞迴特徵消去(support vector machine recursive feature elimination)用於分類預測模型中，而自動關聯決定(automatic relevance determination)這樣的技巧則結合了最小平方支援向量迴歸(least squares support vector regression)用於迴歸預測模型中挑選關鍵外型特徵。使用這樣的方法，不僅可以挑選出關鍵的外型特徵，而這些特徵對情感反應的影響也可以被分析出來，不管是分類或迴歸預測模型中的關鍵外型特徵挑選，都對產品開發的過程有很大的助益。
This study deals with the variable selection problem arises in the context of Kansei engineering system (KES). This problem consists of two sub-problems, namely affective response dimension selection (ARDS) and product form feature selection (PFFS). The crux of the ARRS problem is how to choose suitable adjectives to describe consumers’ affective responses (CARs), while the PFFS problem aims to pin point critical product form features (PFFs) that influence CARs for the product design. In order to select representative adjectives for describing CARs, a method based on factor analysis (FA) and Procrustes analysis is proposed. The selected adjectives can be used in the following semantic differential (SD) experiment to obtain the CAR data. Two kinds of prediction models can be constructed to relate the PFFs and CARs, including the classification based model and the regression based model. On the one hand, using the multiclass fuzzy support vector machine (SVM), the classification based prediction model is capable to correctly discriminate different CARs according to the input PFFs. On the other hand, the regression based prediction model is constructed using support vector regression (SVR) by regarding the PFFs as input vectors and CAR as the predictive output. For selecting critical form features, a hard-wrapper feature selection method, support vector machine recursive feature elimination (SVM-RFE), is adapted to develop the classification based PFFS approach, while a soft-embedded feature selection method, automatic relevance determination (ARD), combined with least squares support vector regression (LS-SVR) is used to construct the regression based PFFS approach. Not only the critical form feature can be selected but also their influence to produce specific ARs can be extracted. Either the methodology of classification based or regression based PFFS is beneficial to the product development process.
Alcantara, E., Artacho, M. A., Gonzalez, J. C., & Garcia, A. C. (2005). Application of product semantics to footwear design. Part I—Identification of footwear semantic space applying differential semantics. International Journal of Industrial Ergonomics, 35, 713-725.
Andrade, J. M., Gomez-Carracedo, M. P., Krzanowski, W., & Kubista, M. (2004). Procrustes rotation in analytical chemistry, a tutorial. Chemometrics and Intelligent Laboratory Systems, 72, 123-132.
Basak, J., De, R. K., & Pal, S. K. (1998). Unsupervised feature selection using a neuro-fuzzy approach. Pattern Recognition Letters, 19, 997-1006.
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 34, 144-155.
Berrar, D., Bradbury, I., & Dubitzky, W. (2006). Avoiding model selection bias in small-sample genomic datasets. Bioinformatics, 22(10), 1245-1250.
Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97, 245-271.
Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Jackel, L., LeCun, Y., Muller, U., Sackinger, E., Simard, P., & Vapnik, V. (1994). Comparison of classifier methods: a case study in handwriting digit recognition. In International Conference on Pattern Recognition, IEEE Computer Society Press.
Bradley, P. S., & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines. In Proceedings of 15th International Conference on Machine Learning, California: San Francisco.
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1983). CART: Classification and Regression Trees. CBelmont, California: Wadsworth.
Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 955-974.
Chen, K. (1997). Form language and style description. Design studies, 18, 249-274.
Chen, K. (1998). A study of computer-supported formal design. Design studies, 19, 331-359.
Chu, W., Keerthi, S. S., Ong, C. J., & Ghahramani, Z. (2006). Bayesian support vector machines for feature ranking and selection. In: Guyon, I., S. Gunn, M. Nikravesh and L. Zadeh, Feature Extraction, Foundations and Applications (pp. Springer Verlag.
Chuang, M. C., Chang, C. C., & Hsu, S. H. (2001). Perceptual factors underlying user preferences toward product form of mobile phones. International Journal of Industrial Ergonomics, 27, 247-258.
Chuang, M. C., & Ma, Y. C. (2001). Expressing the expected product images in product design of micro-electronic products. International Journal of Industrial Ergonomics, 27, 233-245.
Coxhead, P., & Bynner, J. M. (1981). Factor analysis of semantic differential data. Quality & Quantity, 15, 553-567.
Deb, K., & Agrawal, R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems, 9, 115-148.
Deb, K., & Goyal, M. (1996). A combined genetic adaptive search (GeneAS) for engineering design. Computer Sciences and Informatics, 26(4), 30-45.
Duan, K. B., & Keerthi, S. S. (2005). Which is the best multiclass SVM method? An empirical study. Multiple Classifier Systems, 278-285.
Duan, K. B., Rajapakse, J. C., Wang, H., & Azuaje, F. (2005). Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Transactions of Nanobioscience, 4(3), 228-234.
Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis. New York: Wiley.
Evgeniou, T., Pontil, M., Papageorgiou, C., & Poggio, T. (2003). Image representation and feature selection for multimedia database search. IEEE Transactions of Knowledge Data Engineering, 15(4), 911-920.
Grandvalet, Y., & Canu, S. (2003). Adaptive scaling for feature selection in SVMs. In: Becker, S., S. Thrun and K. Obermayer, Advances in Neural Information Processing Systems 15 (pp. 569-576) MIT Press.
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1-3), 389-422.
Han, S. H., & Hong, S. W. (2003). A systematic approach for coupling user satisfaction with product design. Ergonomics, 46(13/14), 1441-1461.
Han, S. H., & Kim, J. (2003). A comparison of screening methods: selecting important design variables for modeling product usability. International Journal of Industrial Ergonomics, 32, 189-198.
Han, S. H., Kim, K. J., Yun, M. H., & Hong, S. W. (2004). Identifying mobile phone design features critical to user satisfaction. Human Factors and Ergonomics in Manufacturing, 14(1), 15-29.
Han, S. H., & Yang, H. (2004). Screening important design variables for building a usability model. International Journal of Industrial Ergonomics, 33, 159-171.
Herbrich, R. (2002). Learning Kernel Classifier: Theory and Algorithms. Cambridge: MIT Press.
Hermes, L., & Buhmann, J. M. (2000). Feature selection for support vector machines. In 15th International Conference on Pattern Recognition.
Hsiao, K. A., & Chen, L. L. (2006). Fundamental dimensions of affective responses to product shapes. International Journal of Industrial Ergonomics, 36, 553-564.
Hsiao, S. W., & Chen, C. H. (1997). A semantic and shape grammar based approach for product design. Design studies, 18, 275-296.
Hsiao, S. W., & Huang, H. C. (2002). A neural network based approach for product form design. Design studies, 23, 67-84.
Hsiao, S. W., & Liu, E. (2004). A neurofuzzy-evolutionary approach for product design. Integrated Computer-Aided Engineering, 11, 323-338.
Hsiao, S. W., & Tsai, H. C. (2005). Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics, 35, 411-428.
Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). Technical Report, A practical guide to support vector classification, Department of Computer Science & Information Engineering, National Taiwan University, Taiwan.
Hsu, C. W., & Lin, C. J. (2001). A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13, 415-425.
Hsu, S. H., Chuang, M. C., & Chang, C. C. (2000). A semantic differential study of designers' and users' product form perception. International Journal of Industrial Ergonomics, 25, 375-391.
Jensen, R. (2005). Combining rough and fuzzy sets for feature selection. School of Informatics, University of Edinburgh. Doctor of Philosophy.
Jindo, T., Hirasago, K., & Nagamachi, M. (1995). Development of a design support system for office chairs using 3-D graphics. International Journal of Industrial Ergonomics, 15, 49-62.
Jones, J. C. (1992). Design Methods. New York: Van Nostrand Reinhold.
Juang, B. H., & Katagiri, S. (1992). Discriminative learning for minimum error classification. IEEE Transactions of Signal Process, 40(12), 3043-3054.
Kim, J. O., & Mueller, C. W. (1978). Factor Analysis: Statistical Methods and Practical Issues. Newbury Park: Sage Publication.
Kohavi, R., & John, G. (1997). Wrapper for feature subset selection. Artificial Intelligence, 97(1-2), 273-324.
Krebel, U. (1999). Pairwise classification and support vector machines. In: Scholkopf, B., J. C. Burges and A. J. Smola, Advances in kernel methods - support vector learning (pp. 255-268) Cambridge: MIT Press.
Krzanowski, W. J. (1987). Selection of variables to preserve multivariate data structure, using principal components. Applied Statistics, 36(1), 22-33.
Kwahk, J., & Han, S. H. (2002). A methodology for evaluating the usability of audiovisual consumer electronic products. Applied Ergonomics, 33, 419-431.
Lai, H. H., Lin, Y. C., & Yeh, C. H. (2006). Form design of product image using grey relational analysis and neural network models. Computers & Operations Research, 32, 2689-2711.
Lai, H. H., Lin, Y. C., Yeh, C. H., & Wei, C. H. (2006). User-oriented design for the optimal combination on product design. International Journal of Production Economics, 100, 253-267.
Lamb, J. M., & Kallal, M. J. (1992). A conceptual framework for apparel design. Clothing & Textiles Research Journal, 10(2), 42-47.
LeCun, Y., Denker, J., Solla, S., Howard, R., & Jackel, L. D. (1990). Optimal brain damages. In: Touretzky, D. S., Advances in Neural Information Processing Systems (pp. 598-605) CA: Mateo: Morgan Kaufmann.
Lin, C. F., & Wang, S. D. (2002). Fuzzy support vector machines. IEEE Transactions on Neural Networks, 13(2), 464-471.
Liu, Y., & Zheng, Y. F. (2006). FS_SFS: A novel feature selection method for support vector machines. Pattern Recognition, 39, 1333-1345.
Lopez, G., Batlles, F. J., & Tovar-Pescador, J. (2005). Selection of input parameters to model direct solar irradiance by using artificial neural networks. Energy, 30, 1675-1684.
MacKay, D. (1994). Bayesian non-linear modelling for the prediction competition. ASHRAE Transactions, 100, 1053-1062.
MacKay, D. (2006). Chemometrics, econometrics, psychometrics—How best to handle hedonics? Food Quality and Preference, 17, 529-535.
Mao, K. Z. (2004). Feature subset selection for support vector machines through discriminative function pruning analysis. IEEE Transactions of System, Man and Cybernetics, 34(1), 60-67.
Mao, Y., Zhou, X., Pi, D., Sun, Y., & Wong, S. T. C. (2005). Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection. Journal of Biomedicine and Biotechnology, 2, 160-171.
Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63, 81-97.
Nagamachi, M. (1993). Kansei Engineering. Tokyo: Kaibundo Publisher.
Nagamachi, M. (1995). Kansei Engineering: a new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15(1), 311-346.
Nunnally, J. C. (1967). Psychometric Theory. USA: McGraw-Hill.
Osgood, C. E., Suci, C. J., & Tannenbaum, P. H. (1957). The Measurement of Meaning. Champaign, IL: University of Illinois Press.
Pal, S. K., Basak, J., & De, R. K. (1996). Feature selection: a neuro-fuzzy approach. In IEEE International Conference on Neural Networks, Washington, DC.
Park, J., & Han, S. H. (2004). A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design. International Journal of Industrial Ergonomics, 34, 31-47.
Platt, J. C., Cristianini, N., & Shawe-Taylor, J. (2000). Large margin DAGs for multiclass classification. In Advances in Neural Information Processing Systems, Cambridge: MIT Press.
Rakotomamonjy, A. (2003). Variable selection using SVM-based criteria. Journal of Machine Learning Research, 3, 1357-1370.
Sahmer, K., & Qannari, E. M. (2008). Procedures for the selection of a subset of attributes in sensory profiling. Food Quality and Preference, 19, 141-145.
Sahmer, K., Vigneau, E., & Qannari, E. M. (2006). A cluster approach to analyze preference data: Choice of the number of clusters. Food Quality and Preference, 17, 257-265.
Scholkopf, B., Guyon, I., & Wetson, J. (2003). Statistical learning and kernel methods in bioinformatics: The Netherlands: IOS Press Amsterdam.
Schutte, S., Eklund, J., Axelsson, J., & Nagamachi, M. (2004). Concepts, methods and tools in Kansei engineering. Theoretical Issues in Ergonomics Sciences, 5(3), 214-231.
Shimizu, Y., & Jindo, T. (1995). A fuzzy logic analysis method for evaluating human sensitivities. International Journal of Industrial Ergonomics, 15, 39-47.
Smith, W. (1956). Product differentiation and market segmentation as an alternative marketing strategy. Journal of Marketing, 21(1), 3-8.
Suykens, J. A. K., Gestel, T. V., Brabanter, D., Moor, B. D., & Vandewalle, J. (2002). Least Squares Support Vector Machines. Singapore: World Scientific.
Thissen, U., R. Van Brakel, A. P. De Weijer, Melssen, W. J., & Buydens, L. M. C. (2003). Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems, 1-2, 35-49.
Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of Royal Statistical Society Series B, 58(1), 267-288.
Vapnik, V. N. (1995). The nature of statistical learning theory New York: Springer.
Vapnik, V. N., Golowich, S., & Smola, A. (1997). Support vector method for function approximation, regression estimation and signal processing. In: Mozer, M., M. Jordan and T. Petsche, Advance in neural information processing system 9 (pp. 281-287) Cambridge, MA: MIT Press.
Viaene, S., Dedene, G., & Derrig, R. A. (2005). Auto claim fraud detection using Bayesian learning neural networks. Expert Systems with Applications, 29, 653-666.
Vigneau, E., & Qannari, E. M. (2003). Clustering of variables around latent components. Communications in Statistics-Simulation and Computation 32(4), 1131-1150.
Wakaki, T., Itakura, H., & Tamura, M. (2004). Rough set-aided feature selection for automatic web-page classification. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence.
Wallace, D. R. (1991). A computer model of aesthetic industrial design, Massachusetts Institute of Technology.
Wang, D., & Lu, W. Z. (2006). Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination model. Chemosphere, 62, 1600-1611.
Wang, W., Xu, Z., Lu, W., & Zhang, X. (2003). Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing, 55, 643-663.
Yamamoto, K., Kojima, T., Yoshikawa, T., & Furuhashi, T. (2005). A basic study on discovering relationships of impression words among individuals using visualization method. In IEEE Workshop on Advanced Robotics and its Social Impacts.
You, H., Ryu, T., Oh, K., Yun, M. H., & Kim, K. J. (2006). Development of customer satisfaction models for automotive interior materials. International Journal of Industrial Ergonomics, 36, 323-330.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：705（臺灣：690，外國：15） ／ 本年度總使用人次：2413552 ／ 訪客人次（自102年09月）：68213919
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上