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DEA-Based Piecewise Linear Discriminant Analysis

Author

Listed:
  • Ai-bing Ji

    (Hebei University)

  • Ye Ji

    (Hebei University
    Moody Analytics Company)

  • Yanhua Qiao

    (Hebei University)

Abstract

Nonlinear classification models have better classification performance than the linear classifiers. However, for many nonlinear classification problems, piecewise-linear discriminant functions can approximate nonlinear discriminant functions. In this study, we combine the algorithm of data envelopment analysis (DEA) with classification information, and propose a novel DEA-based classifier to construct a piecewise-linear discriminant function, in this classifier, the nonnegative conditions of DEA model are loosed and class information is added; Finally, experiments are performed using a UCI data set to demonstrate the accuracy and efficiency of the proposed model.

Suggested Citation

  • Ai-bing Ji & Ye Ji & Yanhua Qiao, 2018. "DEA-Based Piecewise Linear Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 809-820, April.
    • Handle: RePEc:kap:compec:v:51:y:2018:i:4:d:10.1007_s10614-016-9642-8
    DOI: 10.1007/s10614-016-9642-8
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    References listed on IDEAS

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