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General Information
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2014Vol.4(5): 383-389 ISSN: 2010-3697
DOI: 10.7763/IJMO.2014.V4.405

Intelligent Credit Assessment System by Kernel Locality Preserving Projections and Manifold-Regularized SVM Models

Shian-Chang Huang
Abstract—Support vector machines (SVM) have been successfully applied in numerous areas of pattern recognitions, and have demonstrated excellent performance. However, traditional SVM does not make efficient use of both labeled training data and unlabeled testing data. Moreover, one usually encounters high dimensional and nonlinear distributed data in classification problems, especially in financial credit rating assessments. They generally degrade the performance of a classifier due to the curse of dimensionality. This study addresses these problems by proposing a novel intelligent system which integrates a kernel locality preserving projection (KLPP) with a data-dependent manifold-regularized SVM. KLPP is employed to gain a perfect approximation of data manifold and simultaneously preserve local within-class geometric structures according to prior class-label information. Empirical results indicate that, compared with other dimensionality reduction methods and conventional classifiers, the hybrid classifier performs best.

Index Terms—Credit rating, dimensionality reduction, kernel locality preserving projections, subspace analysis, semi-supervised SVM.

Shian-Chang Huang is with the Department of Business Administration, National Changhua University of Education, Changhua, Taiwan (e-mail: shhuang@cc.ncue.edu.tw).

[PDF]

Cite: Shian-Chang Huang, "An Intelligent Credit Assessment System by Kernel Locality Preserving Projections and Manifold-Regularized SVM Models," International Journal of Modeling and Optimization vol. 4, no. 5, pp. 383-389, 2014.

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