DEA-Based Piecewise Linear Discriminant Analysis

Ai-bing Ji (), Ye Ji () and Yanhua Qiao
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Ai-bing Ji: Hebei University
Ye Ji: Hebei University
Yanhua Qiao: Hebei University

Computational Economics, 2018, vol. 51, issue 4, No 3, 809-820

Abstract: 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.

Keywords: Data envelopment analysis; Classification; DEA classification machine; Piecewise-linear discriminant analysis (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10614-016-9642-8

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