Abstract
Credit risk analysis is an essential topic in the financial risk management. Credit risk analysis has been the main focus of financial and banking industry. A number of experiments have been conducted using representative supervised learning algorithms, which were trained using two public available credit datasets. The decision of which specific method to choose is a complex problem. Another option instead of choosing only one method is to create a hybrid ensemble of classifiers.
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Kamos, E., Matthaiou, F., Kotsiantis, S. (2012). Credit Rating Using a Hybrid Voting Ensemble. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_21
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DOI: https://doi.org/10.1007/978-3-642-30448-4_21
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