Credit quality assessments using manifold based semi-supervised discriminant analysis and support vector machines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the large scale of financial data in credit quality forecasting, dimensionality reduction is a key step to enhance classifier performance. By using manifold based semi-supervised discriminant analysis (SSDA) and support vector machines, this study develops a novel prediction system for credit quality assessment, where SSDA makes efficient use of labeled and unlabeled (testing) data points to gain a perfect low dimensional approximation of data manifold and simultaneously maintain the discriminating power. More specifically, the labeled data points are used to maximize the separability between different classes, and the testing data points are used to estimate the intrinsic geometric structure of the data space. Empirical results indicate that SSDA outperforms other dimensionality reduction methods with a significant performance improvement, and our hybrid classifier substantially outperforms other conventional classifiers.

Original languageEnglish
Title of host publicationProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Pages2037-2041
Number of pages5
Volume4
DOIs
Publication statusPublished - 2011 Oct 6
Event2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
Duration: 2011 Jul 262011 Jul 28

Other

Other2011 7th International Conference on Natural Computation, ICNC 2011
CountryChina
CityShanghai
Period11-07-2611-07-28

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Neuroscience(all)

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    Huang, S-C., & Wu, T-K. (2011). Credit quality assessments using manifold based semi-supervised discriminant analysis and support vector machines. In Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011 (Vol. 4, pp. 2037-2041). [6022386] https://doi.org/10.1109/ICNC.2011.6022386