Prediction of wool knitwear pilling propensity using support vector machines

Yap, P, Wang, X, Wang, L and Ong, K 2010, '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
Wang, X
Wang, L
Ong, K
Year 2010
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.
Keyword(s) data mining
knits
pilling
pilling prediction
support vector machines
wool
DOI - identifier 10.1177/0040517509102226
Copyright notice © The Author(s), 2010.
ISSN 0040-5175
Versions
Version Filter Type
Altmetric details:
Access Statistics: 82 Abstract Views  -  Detailed Statistics
Created: Wed, 17 Nov 2010, 16:09:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us