Credit rating using a hybrid voting ensemble

Authors:
Elias Kamos;Foteini Matthaiou;Sotiris Kotsiantis
Affiliations:
Hellenic Open University, Greece;Hellenic Open University, Greece;Department of Mathematics, University of Patras, Greece
Venue:
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Year:
2012

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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.