Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Democracy in neural nets: voting schemes for classification
Neural Networks
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Predictive model of insolvency risk for Australian corporations
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
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As primary focus of banking regulation and supervision is being shifted toward internal risk management for all commercial banks, financial data mining task such as an early warning of bank failure becomes more critical than ever. In this study, we examine the effect of variable selection methods for intelligent bankruptcy prediction models. Moreover, an augmented stacked generalizer that utilizes diversified feature subsets during its learning phase is suggested as an effective ensemble method for promoting independencies among base prediction models. Empirical results show that the augmented stacked generalizer significantly improves overall predictability by reducing the more costly type-I error rate compared against both popular bagging and standard stacking procedures.