Multilayer feedforward networks are universal approximators
Neural Networks
Self organizing neural networks for financial diagnosis
Decision Support Systems
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Variable selection using svm based criteria
The Journal of Machine Learning Research
Bayesian Models for Early Warning of Bank Failures
Management Science
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
An experimental comparison of performance measures for classification
Pattern Recognition Letters
A selective ensemble based on expected probabilities for bankruptcy prediction
Expert Systems with Applications: An International Journal
Using partial least squares and support vector machines for bankruptcy prediction
Expert Systems with Applications: An International Journal
Comparative analysis of data mining methods for bankruptcy prediction
Decision Support Systems
Performance of classification models from a user perspective
Decision Support Systems
International Journal of Hybrid Intelligent Systems
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This paper uses Partial Least Square Discriminant Analysis (PLS-DA) for the prediction of the 2008 USA banking crisis. PLS regression transforms a set of correlated explanatory variables into a new set of uncorrelated variables, which is appropriate in the presence of multicollinearity. PLS-DA performs a PLS regression with a dichotomous dependent variable. The performance of this technique is compared to the performance of 8 algorithms widely used in bankruptcy prediction. In terms of accuracy, precision, F-score, Type I error and Type II error, results are similar; no algorithm outperforms the others. Behind performance, each algorithm assigns a score to each bank and classifies it as solvent or failed. These results have been analyzed by means of contingency tables, correlations, cluster analysis and reduction dimensionality techniques. PLS-DA results are very close to those obtained by Linear Discriminant Analysis and Support Vector Machine.