Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Lazy learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Issues in stacked generalization
Journal of Artificial Intelligence Research
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
Combinations of weak classifiers
IEEE Transactions on Neural Networks
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Credit risk analysis is an important topic in financial risk management. Owing to recent financial crises, credit risk analysis has been the major focus of the financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. To this end, a number of experiments have been conducted using representative learning algorithms, which were tested using two publicly credit datasets. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a selective voting methodology and achieves better performance than any examined simple and ensemble method.