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.
