Integrating nonlinear graph based dimensionality reduction schemes with SVMs for credit rating forecasting

研究成果: Article

21 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)7515-7518
頁數4
期刊Expert Systems with Applications
36
發行號4
DOIs
出版狀態Published - 2009 五月 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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