A New Tool for Feature Extraction and Its Application to Credit Risk Analysis
The aim of this paper is to present a new feature extraction method. Our method is an extension of the classical Partial Least Squares (PLS) algorithm. However, a new weighted separation criterion is applied which is based on the within and between scatter matrices. In order to compare the performance of the classification the economical datasets are used.
KeywordsClassification Credit risk Feature extraction Partial Least Square Separation criterion
- Duda R, Hart P (2000) Pattern Classification. John Wiley & Sons, New YorkGoogle Scholar
- Gren J. (1987) Mathematical Statistic. PWN, WarsawGoogle Scholar
- Quinlan J R (1993) C4.5: Programs for Machine Learning. Morgan KaufmannGoogle Scholar
- Shawe-Taylor J, Cristianini N (2004) Kernel Methods for Pattern Analysis., Cambridge Univ. Press, CambridgeGoogle Scholar
- Wold H (1975) Soft Modeling by Latent Variables: The Non-Linear Iterative Partial Least Squares (NIPALS). Approach Perspectives in Probability and Statistics. Papers in Honour of M.S. Bartlett, 117–142Google Scholar