Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence
A new approach for classification of circular knitted fabric defect is proposed which is based on accepting uncertainty in labels of the learning data. In the basic classification methodologies it is assumed that correct labels are assigned to samples and these approaches concentrate on the strength of categorization. However, there are some classification problems in which a considerable amount of uncertainty exists in the labels of samples. The core of innovation in this research has been usage of the uncertain information of labeling and their combination with the Dempster-Shafer theory of evidence. The experimental results show the robustness of the proposed method in comparison with usual classification techniques of supervised learning where the certain labels are assigned to training data. (C) 2010 Elsevier Ltd. All rights reserved.
theory of evidence circular knitted fabric defect uncertainty in labels wavelet transform k-nearest neighbors mlp neural network