Improved classification model via MPEC

doi:10.7523/j.issn.2095-6134.2009.5.003

›› 2009, Vol. 26 ›› Issue (5): 599-608.DOI: 10.7523/j.issn.2095-6134.2009.5.003

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Improved classification model via MPEC

DING Fei^1,2, YIN Hong-Xia^2,3

1. School of Mathematics, Graduate University of the Chinese Academy of Sciences, Beijing 100049, China;
2. Research Center on Fictitious Economy and Date Science, Chinese Academy of Sciences, Beijing 100080, China;
3. Department of Mathematics and Statistics, Minnesota State University Mankato, Mankato, MN 56001, USA

Received:2009-03-18 Revised:2009-04-20 Online:2009-09-15

Abstract

Abstract:

In this paper, we provide an improved form of MPEC model for data classification first proposed in Ref.[1]. We use β likelihood estimation instead of maximum likelihood estimation to estimate the parameters of data's probability distribution function (modeled by Gaussian mixture models). Our new model can avoid the contingent of unboundedness of the maximum likelihood function and excessive sensitivity of the maximum likelihood estimator to outliers, showing more robustness. Then we use filterSQP method to solve our β likelihood MPEC model as nonlinear program. Efficiency of the model is shown by primal numerical tests.

Key words: MPEC, β likelihood estimation, filterSQP, robustness

CLC Number:

O221.2

DING Fei, YIN Hong-Xia. Improved classification model via MPEC[J]. , 2009, 26(5): 599-608.

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Improved classification model via MPEC

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