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研究生:楊智傑
研究生(外文):Chih-Chieh Yang
論文名稱:感性工學系統之變數篩選研究
論文名稱(外文):A Study on Variable Selection for Kansei Engineering System
指導教授:謝孟達謝孟達引用關係
指導教授(外文):Meng-Dar Shieh
學位類別:博士
校院名稱:國立成功大學
系所名稱:工業設計學系碩博士班
學門:設計學門
學類:產品設計學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:123
中文關鍵詞: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
外文關鍵詞:Variable selectionProduct form feature selectionKansei engineering systemAffective response dimension selection
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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.
ABSTRACT....................I
ACKOWLEDGEMENTS....................III
LIST OF CONTENTS....................IV
LIST OF TABLES....................VIII
LIST OF FIGURES....................IX
ABBREVIATIONS AND SYMBOLS....................XI

1 INTRODUCTION....................1
1.1 MOTIVATION....................1
1.2 RESEARCH BACKGROUND....................2
1.2.1 A General Framework of Kansei Engineering System....................2
1.2.2 Variable Selection Problem in Kansei Engineering System....................6
1.3 OBJECTIVES, SCOPE AND LIMITATIONS....................8
1.4 ORGANIZATION OF THE THESIS....................9

2 LITERATURE REVIEW....................12
2.1 DESCRIPTION OF AFFECTIVE RESPONSES WITH ADJECTIVES....................12
2.2 PRODUCT FORM FEATURE REPRESENTATION....................13
2.3 NONLINEAR PREDICTION MODEL OF AFFECTIVE RESPONSES....................16
2.4 SELECTION OF REPRESENTATIVE AFFECTIVE RESPONSE DIMENSIONS....................18
2.5 SELECTION OF CRITICAL PRODUCT FORM FEATURES....................19

3 THEORETICAL BACKGROUND....................22
3.1 FEATURE EXTRACTION FOR SEMANTIC DIFFERENTIAL DATA....................22
3.1.1 Factor Analysis for Extracting Factor Loadings....................22
3.1.2 Procrustes Analysis for Comparing Data Matrices....................23
3.2 SUPPORT VECTOR MACHINES CLASSIFICATION MODEL....................24
3.2.1 Fuzzy Support Vector Machines for Binary Classification....................24
3.2.2 Using Kernel Functions....................26
3.2.3 One-Versus-One Multiclass Support Vector Machines....................27
3.2.4 Feature Selection Based on Multiclass SVM-RFE....................28
3.3 SUPPORT VECTOR MACHINES REGRESSION MODEL....................30
3.3.1 Support Vector Regression....................30
3.3.2 Least Squares Support Vector Regression....................33
3.3.3 Feature Selection Based on Automatic Relevance Determination....................34

4 AFFECTIVE RESPONSE DIMENSION SELECTION USING FACTOR ANALYSIS AND PROCRUSTES ANALYSIS....................36
4.1 INTRODUCTION....................36
4.2 IMPLEMENTATION PROCEDURES....................36
4.2.1 Selection of Representative Products....................36
4.2.2 Preparation of Initial Affective Response Dimensions....................37
4.2.3 Experiment and Questionnaire Design....................37
4.2.4 Analyzing Initial Affective Response Dimensions Using Factor Analysis....................38
4.2.5 The Procrustes Analysis Process for ARDS....................38
4.3 EXPERIMENTAL RESULTS....................39
4.3.1 Results of Factor Analysis....................39
4.3.2 Analysis of the Selection Process Using Procrustes Analysis....................41
4.3.3 Results of the Adjective Ranking....................45
4.4 SUMMARY....................47

5 CLASSIFICATION MODEL OF PRODUCT FORM DESIGN USING MULTICLASS FUZZY SVM....................49
5.1 INTRODUCTION....................49
5.2 IMPLEMENTATION PROCEDURES....................50
5.2.1 Experiment and Questionnaire Design....................50
5.2.2 Constructing OVO Fuzzy SVM Model....................50
5.2.3 Choosing Optimal Parameters Using Cross-Validation....................51
5.3 EXPERIMENTAL RESULTS....................52
5.3.1 Comparison of the Training Effect of Fuzzy SVM and SVM Using Different Kernel Functions....................52
5.3.2 Analysis of Cross-Validation Process of Fuzzy SVM and SVM....................53
5.3.3 Performance of the Optimal Training Model....................59
5.4 SUMMARY....................60

6 PRODUCT FORM FEATURE SELECTION USING MULTICLASS SVM-RFE....................62
6.1 INTRODUCTION....................62
6.2 IMPLEMENTATION PROCEDURES....................62
6.2.1 Constructing the Multiclass Fuzzy SVM Model....................63
6.2.2 The Multiclass SVM-RFE Process for PFFS....................63
6.3 EXPERIMENTAL RESULTS....................65
6.3.1 Determination of Optimal Training Model....................65
6.3.2 Analysis of Feature Ranking....................66
6.3.3 Performance of Selected Feature Subset....................68
6.3.4 Weight Distribution Analysis of Overall Ranking....................70
6.3.5 Weight Distribution Analysis of Class-Specific Ranking....................73
6.4 SUMMARY....................75

7 AN SVR-BASED PREDICTION MODEL OF AFFECTIVE RESPONSES FOR PRODUCT FORM DESIGN....................76
7.1 INTRODUCTION....................76
7.2 IMPLEMENTATION PROCEDURES....................76
7.2.1 Experiment and Questionnaire Design....................76
7.2.2 Constructing the Support Vector Regression Prediction Model....................77
7.2.3 Optimizing Training Parameters Using Real-Coded Genetic Algorithm....................78
7.3 EXPERIMENTAL RESULTS....................80
7.3.1 Analysis of the Optimization Process Using RCGA....................80
7.3.2 Predictive Performance of Different Kernel Functions....................82
7.4 SUMMARY....................84

8 PRODUCT FORM FEATURE SELECTION USING LS-SVR AND ARD....................85
8.1 INTRODUCTION....................85
8.2 IMPLEMENTATION PROCEDURES....................86
8.2.1 Construction of the LS-SVR Prediction Model....................86
8.2.2 The ARD Process for PFFS....................86
8.3 EXPERIMENTAL RESULTS....................88
8.3.1 Effects of Optimization Algorithms for ARD....................88
8.3.2 Predictive Performance of the LS-SVR Model with ARD....................92
8.3.3 Analysis of the Feature Ranking....................94
8.3.4 Relative Relevance Analysis of Features....................97
8.4 SUMMARY....................100

9 CONCLUSIONS....................101
10 FUTURE RESEARCH....................104

APPENDIX....................106
A. MOBILE PHONE DESIGN SAMPLES....................106
B. ENCODING OF PRODUCT FORM FEATURE REPRESENTATION....................109
C. QUESTIONNAIRE INTERFACE....................111

REFERENCES....................114
PUBLICATION....................123
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