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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
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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.
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