A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Pairwise classification and support vector machines
Advances in kernel methods
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Bayesian Models for Early Warning of Bank Failures
Management Science
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Expert Systems with Applications: An International Journal
Multiple classifier application to credit risk assessment
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper presents methods of banks discrimination according to the rate of NonPerforming Loans (NPLs), using Gaussian Bayes models and different approaches of multiclass Support Vector Machines (SVM). This classification problem involves many irrelevant variables and comparatively few training instances. New variable selection strategies are proposed. They are based on Gaussian marginal densities for Bayesian models and ranking scores derived from multiclass SVM. The results on both toy data and real-life problem of banks classification demonstrate a significant improvement of prediction performance using only a few variables. Moreover, Support Vector Machines approaches are shown to be superior to Gaussian Bayes models.