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Prediction of wool knitwear pilling propensity using support vector machines
journal contribution
posted on 2010-01-01, 00:00 authored by Poh Hean Yap, Xungai Wang, Lijing Wang, Kok-Leong OngThe 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.
History
Journal
Textile Research JournalVolume
80Issue
1Pagination
77 - 83Publisher
Sage PublicationsLocation
London, EnglandPublisher DOI
ISSN
0040-5175eISSN
1746-7748Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2010, The AuthorsUsage metrics
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