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Prediction of wool knitwear pilling propensity using support vector machines

Yap, Poh Hean, Wang, Xungai, Wang, Lijing and Ong, Kok-Leong 2010, Prediction of wool knitwear pilling propensity using support vector machines, Textile Research Journal, vol. 80, no. 1, pp. 77-83.

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Title Prediction of wool knitwear pilling propensity using support vector machines
Author(s) Yap, Poh Hean
Wang, Xungai
Wang, Lijing
Ong, Kok-Leong
Journal name Textile Research Journal
Volume number 80
Issue number 1
Start page 77
End page 83
Publisher Sage Publications
Place of publication London, England
Publication date 2010
ISSN 1746-7748
0040-5175
Keyword(s) pilling
pilling prediction
wool
knits
dupport vector machines
data mining
fibre science
Summary The propensity of wool knitwear to form entangled fiber balls, known as pills, on the surface is affected by a large number of factors. This study examines, for the first time, the application of the support vector machine (SVM) data mining tool to the pilling propensity prediction of wool knitwear. The results indicate that by using the binary classification method and the radial basis function (RBF) kernel function, the SVM is able to give high pilling propensity prediction accuracy for wool knitwear without data over-fitting. The study also found that the number of records available for each pill rating greatly affects the learning and prediction capability of SVM models.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2010, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30029062

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Created: Thu, 03 Jun 2010, 12:32:50 EST by Leanne Swaneveld