The data mining techniques such as Artificial
Neural network algorithm (ANN) and support vector machine
(SVM) based models are utilized in this study. Three different
kernel functions were used as SVM kernel functions which are
polynomial, Radial Basis Function (RBF), and Pearson VII
function-based Universal Kernel (PUK). The SVM models
based on these three kernel functions and ANN model were
used in forecasting the quality of ring and compact spinning
yarn such as yarn unevenness, hairiness, yarn tenacity, and
yarn elongation. The comparison of results indicates that the
SVM models based on RBF and PUK Performs yarn
properties forecasts more accurately than ANN model.
Data mining, artificial neural network (ANN),
support vector machine (SVM), kernel functions, yarn
The authors are with the College of Textiles, Donghua University,
Shanghai 201620, People's Republic of China (e-mail:
Khalid A. A. Abakar and Chongwen Yu, "
The Performance of Data Mining Techniques in
Prediction of Yarn Quality," International Journal of Innovation, Management and Technology vol. 4, no. 4, pp.