National Changhua University of Education Institutional Repository : Item 987654321/17071
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题名: Using SVMs with Embedded Recursive Feature Selections for Credit Rating Forecasting
作者: Huang, Shian-Chang;Huang, Ming-Hsiang
贡献者: 企業管理學系
关键词: Support vector machine;Feature selection;Multi-class classification;Credit rating;Credit risk
日期: 2010-01
上传时间: 2013-07-11T09:04:30Z
出版者: Taru Publications
摘要: This study embedded a recursive feature selection scheme in support vector machines (SVM) for credit rating forecasting. SVMs have been successfully applied in numerous areas, and have demonstrated excellent performance. However, due to the high dimensionality of our input variables, this study employed a fast recursive feature selection algorithm to eliminate irrelevant features and enhance the performance of SVM classifiers. Empirical results have indicated that one-vs-one SVM with embedded recursive feature selection outperforms other multi-class SVMs. Compared to traditional multi-class classifiers, the performance improvement owing to embedded recursive feature selections is significant.
關聯: Journal of Statistics & Management Systems, 13(1): 165-177
显示于类别:[企業管理學系] 期刊論文

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