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IPM
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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 11610
School of Cognitive Sciences
  Title:   Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence
  Author(s): 
1.  Mahdi Tabassian
2.  Reza Ghaderi
3.  Reza Ebrahimpour
  Status:   Published
  Journal: Expert Systems with Applications
  Vol.:  38
  Year:  2011
  Pages:   5259-5267
  Supported by:  IPM
  Abstract:
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.

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