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Decision Boundary Extraction of Classifiers

Journal of physics. Conference series, 2020-11, Vol.1651 (1), p.12031 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1651/1/012031

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  • Title:
    Decision Boundary Extraction of Classifiers
  • Author: Gong, Ningyuan ; Wang, Zixiong ; Chen, Shuangmin ; Liu, Guozhu ; Xin, Shiqing
  • Subjects: Accuracy ; Algorithms ; Boundaries ; Classification ; Classifiers ; Decision analysis ; Decision trees ; Machine learning ; Smoothness ; Voronoi graphs
  • Is Part Of: Journal of physics. Conference series, 2020-11, Vol.1651 (1), p.12031
  • Description: In the field of machine learning, explicitly extracting the decision boundary of a classifier not only helps to visualize the differences between different classifiers, but also provides a more convenient way to determine the class of a sample. We proposed a boundary extraction method that relies on the output result of the classifier but not on the classifier mechanism. We find the initial decision boundary based on Voronoi diagram, and then take the smoothness and simplicity as the driving goal, adaptively adjust the shape of the boundary until convergence. In order to verify the effectiveness and usefulness of the algorithm, the decision boundaries generated by four different classifiers, ANN, SVM, Random Forest, and ELM, were visualized on the four data sets, the classification accuracy is also analyzed based on the extracted decision boundaries. The test results show that the visualization results of decision boundaries and the classification accuracy based on explicit decision boundaries are highly consistent with the classification accuracy of the classifier, which can simulate the decision mechanism of the classifier well.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1651/1/012031
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    IOP 英国物理学会OA刊
    ProQuest Central
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