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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
頁面2037-2041
頁數5
4
DOIs
出版狀態Published - 2011 十月 6
事件2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
持續時間: 2011 七月 262011 七月 28

Other

Other2011 7th International Conference on Natural Computation, ICNC 2011
國家China
城市Shanghai
期間11-07-2611-07-28

    指紋

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Neuroscience(all)

引用此

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