The nature of statistical learning theory
The nature of statistical learning theory
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prediction of concrete carbonation depth based on support vector regression
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Financial ratings with scarce information: A neural network approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Orthogonal support vector machine for credit scoring
Engineering Applications of Artificial Intelligence
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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.