Abstract:
In this paper an ensemble of classifiers for credit risk assessment is proposed. A sliding window of samples with pre-specified size is adopted to train each individual c...Show MoreMetadata
Abstract:
In this paper an ensemble of classifiers for credit risk assessment is proposed. A sliding window of samples with pre-specified size is adopted to train each individual classifier of ensemble in a logical ring structure. The completion of one cycle of a logical ring structure is treated as a pass. During every pass by voting mechanism classifier with higher accuracy is maintained. The final accuracy of the ensemble method is determined after completion of all cycles with a meta-voting. We have evaluated the performance of our method on two publicly available credit databases and compared it with two benchmark ensemblers such as Bagging and Boosting. Type-I and Type-II errors of this method suggest the financial institutions to assess their credit risk accurately and make them healthy.
Published in: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Date of Conference: 22-25 August 2013
Date Added to IEEE Xplore: 21 October 2013
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