Paper Info
Title
A support vector regression based prediction model of affective responses for product form design
Abstract
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.
Year
DOI
Venue
2010
10.1016/j.cie.2010.07.019
Computers & Industrial Engineering
Keywords
DocType
Volume
product form design,neural network,5-fold cross-validation,product sample,affective response,support vector regression,genetic algorithm,average car rating,optimal training parameter,kansei engineering,adjective evaluation data,product form feature,prediction model,optimizing training parameter,machine learning,cross validation
Journal
59
Issue
ISSN
Citations 
4
Computers & Industrial Engineering
18
PageRank 
References 
Authors
0.85
6
2
Name
Order
Citations
PageRank
Chih-Chieh Yang112713.88
Meng-Dar Shieh21079.82