Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Machine Learning
Credit Scoring and Its Applications
Credit Scoring and Its Applications
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Expert Systems with Applications: An International Journal
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Use of particle swarm optimization for machinery fault detection
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
A reliability-based RBF network ensemble model for foreign exchange rates predication
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
The most commonly used techniques for credit scoring is logistic regression, and more recent research has proposed that the support vector machine is a more effective method. However, both logistic regression and support vector machine suffers from curse of dimension. In this paper, we introduce a new way to address this problem which is defined as orthogonal dimension reduction. We discuss the related properties of this method in detail and test it against other common statistical approaches-principal component analysis and hybridizing logistic regression to better solve and evaluate the data. With experiments on German data set, there is also an interesting phenomenon with respect to the use of support vector machine, which we define as 'Dimensional interference', and discuss in general. Based on the results of cross-validation, it can be found that through the use of logistic regression filtering the dummy variables and orthogonal extracting feature, the support vector machine not only reduces complexity and accelerates convergence, but also achieves better performance.