National Changhua University of Education Institutional Repository : Item 987654321/1869

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NCUEIR > College of Management > Department of Business Administration > Periodical Articles > Item 987654321/1869

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/1869

Title:	Integrating Nonlinear Graph Based Dimensionality Reduction Schemes with SVMs for Credit Rating Forecasting
Authors:	Shian-Chang Huang
Contributors:	企業管理學系
Keywords:	Kernel graph embedding Dimensionality reduction Support vector machine Multi-class classification Credit rating
Date:	2009-05
Issue Date:	2010-11-15T07:48:37Z
Abstract:	By integrating graph based nonlinear dimensionality reduction with support vector machines (SVMs), this study develops a novel prediction model for credit ratings forecasting. SVMs have been successfully applied in numerous areas, and have demonstrated excellent performance. However, due to the high dimensionality and nonlinear distribution of the input data, this study employed a kernel graph embedding(KGE) scheme to reduce the dimensionality of input data, and enhance the performance of SVM classifiers. Empirical results indicated that one-vs-one SVM with KGE outperforms other multi-class SVMs and traditional classifiers. Compared with other dimensionality reduction methods the performance improvement owing to KGE is significant.
Relation:	Expert Systems with Applications: An International Journal, 36(4):7515-7518
Appears in Collections:	[Department of Business Administration] Periodical Articles

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