Credit Quality Assessments Using Manifold Based Semi-supervised Discriminant Analysis and Support Vector Machines

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.

作者: Shian-Chang Huang Tung-Kuang Wu

作者单位: Department of Business Administration National Changhua University of Education Changhua, Taiwan Department of Information Management National Changhua University of Education Changhua, Taiwan

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 2065-2069

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）