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Process service quality evaluation based on Dempster-Shafer theory and support vector machine

Author

Listed:
  • Feng-Que Pei
  • Dong-Bo Li
  • Yi-Fei Tong
  • Fei He

Abstract

Human involvement influences traditional service quality evaluations, which triggers an evaluation’s low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM) and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large number of sensors. Preprocessing steps such as feature simplification and normalization are reduced. Based on three individual SVM models, the basic probability assignments (BPAs) are constructed, which can help the evaluation in a qualitative and quantitative way. The process service quality evaluation results are validated by the Dempster rules; the decision threshold to resolve conflicting results is generated from three SVM models. A case study is presented to demonstrate the effectiveness of the SVMs-DS method.

Suggested Citation

  • Feng-Que Pei & Dong-Bo Li & Yi-Fei Tong & Fei He, 2017. "Process service quality evaluation based on Dempster-Shafer theory and support vector machine," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
  • Handle: RePEc:plo:pone00:0189189
    DOI: 10.1371/journal.pone.0189189
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    References listed on IDEAS

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    2. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
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