TY - JOUR
T1 - A support vector regression based prediction model of affective responses for product form design
AU - Yang, Chih Chieh
AU - Shieh, Meng Dar
N1 - Funding Information:
The authors would like to thank Prof. I-Cheng Yeh, the leader of Business Intelligence Laboratory, Chung Hua University, Taiwan, to provide the CAFE neural network software. This work was supported by the National Science Council of the Republic of China under grant NSC96-2221-E-006-126 .
PY - 2010/11
Y1 - 2010/11
N2 - In this paper, a state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers' affective responses (CARs) for product form design. First, pairwise adjectives were used to describe the CARs toward product samples. Second, the product form features (PFFs) were examined systematically and then stored them either as continuous or discrete attributes. The adjective evaluation data of consumers were gathered from questionnaires. Finally, prediction models based on different adjectives were constructed using SVR, which trained a series of PFFs and the average CAR rating of all the respondents. The real-coded genetic algorithm (RCGA) was used to determine the optimal training parameters of SVR. The predictive performance of the SVR with RCGA (SVR-RCGA) is compared to that of SVR with 5-fold cross-validation (SVR-5FCV) and a back-propagation neural network (BPNN) with 5-fold cross-validation (BPNN-5FCV). The experimental results using the data sets on mobile phones and electronic scooters show that SVR performs better than BPNN. Moreover, the RCGA for optimizing training parameters for SVR is more convenient for practical usage in product form design than the timeconsuming CV.
AB - In this paper, a state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers' affective responses (CARs) for product form design. First, pairwise adjectives were used to describe the CARs toward product samples. Second, the product form features (PFFs) were examined systematically and then stored them either as continuous or discrete attributes. The adjective evaluation data of consumers were gathered from questionnaires. Finally, prediction models based on different adjectives were constructed using SVR, which trained a series of PFFs and the average CAR rating of all the respondents. The real-coded genetic algorithm (RCGA) was used to determine the optimal training parameters of SVR. The predictive performance of the SVR with RCGA (SVR-RCGA) is compared to that of SVR with 5-fold cross-validation (SVR-5FCV) and a back-propagation neural network (BPNN) with 5-fold cross-validation (BPNN-5FCV). The experimental results using the data sets on mobile phones and electronic scooters show that SVR performs better than BPNN. Moreover, the RCGA for optimizing training parameters for SVR is more convenient for practical usage in product form design than the timeconsuming CV.
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U2 - 10.1016/j.cie.2010.07.019
DO - 10.1016/j.cie.2010.07.019
M3 - Article
AN - SCOPUS:78049369155
SN - 0360-8352
VL - 59
SP - 682
EP - 689
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
IS - 4
ER -