Two-level classifier ensembles for credit risk assessment

Authors:
A. I. Marqués;V. García;J. S. Sánchez
Affiliations:
Department of Business Administration and Marketing, Universitat Jaume I, Av. Sos Baynat s/n, 12071 Castelló de la Plana, Spain;Department of Computer Languages and Systems, Universitat Jaume I, Av. Sos Baynat s/n, 12071 Castelló de la Plana, Spain;Department of Computer Languages and Systems, Universitat Jaume I, Av. Sos Baynat s/n, 12071 Castelló de la Plana, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been applied to credit scoring problems, demonstrating to be generally more accurate than single prediction models. The present paper goes one step beyond by introducing composite ensembles that jointly use different strategies for diversity induction. Accordingly, the combination of data resampling algorithms (bagging and AdaBoost) and attribute subset selection methods (random subspace and rotation forest) for the construction of composite ensembles is explored with the aim of improving the prediction performance. The experimental results and statistical tests show that this new two-level classifier ensemble constitutes an appropriate solution for credit scoring problems, performing better than the traditional single ensembles and very significantly better than individual classifiers.