Product form feature selection methodology based on numerical definition–based design. (September 2014)
- Record Type:
- Journal Article
- Title:
- Product form feature selection methodology based on numerical definition–based design. (September 2014)
- Main Title:
- Product form feature selection methodology based on numerical definition–based design
- Authors:
- Chen, Hung-Yuan
Yang, Chih-Chieh
Ko, Yao-Tsung
Chang, Yu-Ming
Chang, Hua-Cheng - Abstract:
- In product design, the product form has a significant effect on the affective response it induces in potential consumers and is also of crucial importance if the product is to achieve commercial success. Intuitively, it seems reasonable to speculate that a consumer's affective response to a product is dominated by certain critical features of the product form. To extract the product's specific form features critical to determining consumers' affective responses, this study proposes a product form feature selection methodology based on a numerical definition–based approach and the consumers' affective responses. In the proposed methodology, numerical definition–based approach is used to generate an explicit numerical definition of the product form design, and the corresponding consumers' affective responses (described using single adjectives) are determined by means of a semantic differential experiment. Two consumers' affective response prediction models are constructed using support vector regression and multiple linear regression techniques, respectively. Finally, two feature selection methods, namely, support vector regression with support vector machine–recursive feature elimination and multiple linear regression with the stepwise procedure, are used to identify the critical form features. The validity of the two feature selection methods is demonstrated using a knife design for illustration purposes. The results show that the proposed methodology provides productIn product design, the product form has a significant effect on the affective response it induces in potential consumers and is also of crucial importance if the product is to achieve commercial success. Intuitively, it seems reasonable to speculate that a consumer's affective response to a product is dominated by certain critical features of the product form. To extract the product's specific form features critical to determining consumers' affective responses, this study proposes a product form feature selection methodology based on a numerical definition–based approach and the consumers' affective responses. In the proposed methodology, numerical definition–based approach is used to generate an explicit numerical definition of the product form design, and the corresponding consumers' affective responses (described using single adjectives) are determined by means of a semantic differential experiment. Two consumers' affective response prediction models are constructed using support vector regression and multiple linear regression techniques, respectively. Finally, two feature selection methods, namely, support vector regression with support vector machine–recursive feature elimination and multiple linear regression with the stepwise procedure, are used to identify the critical form features. The validity of the two feature selection methods is demonstrated using a knife design for illustration purposes. The results show that the proposed methodology provides product designers with a powerful tool for systematically extracting the critical form features and evaluating their respective effects on the affective responses. … (more)
- Is Part Of:
- Concurrent engineering, research and applications. Volume 22:Number 3(2014:Sep.)
- Journal:
- Concurrent engineering, research and applications
- Issue:
- Volume 22:Number 3(2014:Sep.)
- Issue Display:
- Volume 22, Issue 3 (2014)
- Year:
- 2014
- Volume:
- 22
- Issue:
- 3
- Issue Sort Value:
- 2014-0022-0003-0000
- Page Start:
- 183
- Page End:
- 196
- Publication Date:
- 2014-09
- Subjects:
- Feature selection -- support vector machine–recursive feature elimination -- multiple linear regression -- consumers' affective response
Production engineering -- Periodicals
Concurrent engineering -- Periodicals
621.39 - Journal URLs:
- http://cer.sagepub.com/ ↗
http://www.uk.sagepub.com/home.nav ↗
http://firstsearch.oclc.org ↗
http://firstsearch.oclc.org/journal=1063-293x;screen=info;ECOIP ↗ - DOI:
- 10.1177/1063293X14534456 ↗
- Languages:
- English
- ISSNs:
- 1063-293X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
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- British Library DSC - BLDSS-3PM
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