A support vector regression based prediction model of affective responses for product form design

Chih Chieh Yang, Meng Dar Shieh

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)682-689
Number of pages8
JournalComputers and Industrial Engineering
Issue number4
Publication statusPublished - 2010 Nov

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

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