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標題Title: Product form feature selection for mobile phone design using LS-SVR and ARD
作者Authors: 楊智傑,Meng-Dar..等
上傳單位Department: 多媒體與電腦娛樂科學系
上傳時間Date: 2009-11-16
上傳者Author: 楊智傑
審核單位Department: 多媒體與電腦娛樂科學系
審核老師Teacher: 楊智傑
檔案類型Categories: 論文Thesis
關鍵詞Keyword: Feature selection, Least squares support vector regression, Automatic relevance determination, Bayesian inference
摘要Abstract: In the product design field, it is important to pin point critical product form features (PFFs) that influence consumers’ affective responses (CARs) of a product design. Manual inspection of critical form features based on expert opinions (such as those of product designers) has not proved to meet with the acceptance of consumers. In this paper, an approach based on least squares support vector regression (LS-SVR) and automatic relevance determination (ARD) is proposed to streamline the task of product form feature selection (PFFS) according to the CAR data. The representation of PFFs is determined by morphological analysis and pairwise adjectives are used to express CARs. In order to gather the CAR data, an experiment of semantic differential (SD) evaluation on collected product samples was conducted. The LS-SVR prediction model can be constructed using the PFFs as input data and the evaluated SD scores as output value. The optimal parameters of the LS-SVR model are tuned by using Bayesian inference. Finally, an ARD selection process is used to analyze the relative relevance of PFFs to obtain feature ranking. A case study of mobile phone design is also given to demonstrate the proposed method. In order to examine the effectiveness of the proposed method, the predictive performance is compared with a typical LS-SVR model using a grid search with leave-one-out cross validation (LOOCV) and a multiple linear regression (MLR). The proposed model based on LS-SVR and ARD is outperformed to the other two model benefits from the soft feature selection mechanism. Furthermore, the resulting feature ranking is also compared with that of the MLR with backward model selection (BMS). The results of these two methods using the CAR data of three adjectives exhibit similar feature ranking. Since only one small data of mobile phone design are investigated, a more comprehensive investigations based on different kinds of products will be needed to verify the proposed method in our future study.

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