On the use of data filtering techniques for credit risk prediction with instance-based models

Authors:
V. GarcíA;A. I. MarquéS;J. S. SáNchez
Affiliations:
Institute of New Imaging Technologies, Department of Computer Languages and Systems, Universitat Jaume I, Av. Sos Baynat s/n, 12071 Castelló de la Plana, Spain;Department of Business Administration and Marketing, Universitat Jaume I, Av. Sos Baynat s/n, 12071 Castelló de la Plana, Spain;Institute of New Imaging Technologies, 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 prediction, from statistical models to artificial intelligence methods. However, very few research efforts have been devoted to deal with the presence of noise and outliers in the training set, which may strongly affect the performance of the prediction model. Accordingly, the aim of the present paper is to systematically investigate whether the application of filtering algorithms leads to an increase in accuracy of instance-based classifiers in the context of credit risk assessment. The experimental results with 20 different algorithms and 8 credit databases show that the filtered sets perform significantly better than the non-preprocessed training sets when using the nearest neighbour decision rule. The experiments also allow to identify which techniques are most robust and accurate when confronted with noisy credit data.