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A sliding window based meta-majority of voting ensemble for credit risk assessment | IEEE Conference Publication | IEEE Xplore

A sliding window based meta-majority of voting ensemble for credit risk assessment


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 More

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.
Date of Conference: 22-25 August 2013
Date Added to IEEE Xplore: 21 October 2013
ISBN Information:
Conference Location: Mysore, India

References

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