Prediction of wool knitwear pilling propensity using support vector machines

Yap, P, Ong, K, Wang, L and Wang, X 2009, 'Prediction of wool knitwear pilling propensity using support vector machines', Textile Research Journal, vol. 80, no. 1, pp. 77-83.


Document type: Journal Article
Collection: Journal Articles

Title Prediction of wool knitwear pilling propensity using support vector machines
Author(s) Yap, P
Ong, K
Wang, L
Wang, X
Year 2009
Journal name Textile Research Journal
Volume number 80
Issue number 1
Start page 77
End page 83
Total pages 7
Publisher Sage Publications Ltd.
Abstract 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.
Subject Textile Technology
Keyword(s) pilling
pilling prediction
wool
knits
support vector machines
data mining
DOI - identifier 10.1177/0040517509102226
Copyright notice © The Author(s), 2010
ISSN 0040-5175
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