A comparative study on base classifiers in ensemble methods for credit scoring

•Small improvements in the systems about credit scoring can suppose great profits.•Ensembles of classifiers achieve the better results for credit risk assessment.•To look for the best base classifier used in ensembles on credit datasets is an important task.•Via experiments, it is shown that the cre...

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Bibliographic Details
Published in:Expert systems with applications 2017-05, Vol.73, p.1-10
Main Authors: Abellán, Joaquín, Castellano, Javier G.
Format: Article
Language:English
Subjects:
Artificial intelligence
Bankruptcy
Base classifier
Classification
Classifiers
Comparative studies
Credit scoring
Decision trees
Ensembles of classifiers
Imprecise Dirichlet model
Risk assessment
Uncertainty measures
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Summary:•Small improvements in the systems about credit scoring can suppose great profits.•Ensembles of classifiers achieve the better results for credit risk assessment.•To look for the best base classifier used in ensembles on credit datasets is an important task.•Via experiments, it is shown that the credal decision tree classifier is the best one to be used in ensembles.•The study uses several of the most successful ensemble schemes and single classifiers. In the last years, the application of artificial intelligence methods on credit risk assessment has meant an improvement over classic methods. Small improvements in the systems about credit scoring and bankruptcy prediction can suppose great profits. Then, any improvement represents a high interest to banks and financial institutions. Recent works show that ensembles of classifiers achieve the better results for this kind of tasks. In this paper, it is extended a previous work about the selection of the best base classifier used in ensembles on credit data sets. It is shown that a very simple base classifier, based on imprecise probabilities and uncertainty measures, attains a better trade-off among some aspects of interest for this type of studies such as accuracy and area under ROC curve (AUC). The AUC measure can be considered as a more appropriate measure in this grounds, where the different type of errors have different costs or consequences. The results shown here present to this simple classifier as an interesting choice to be used as base classifier in ensembles for credit scoring and bankruptcy prediction, proving that not only the individual performance of a classifier is the key point to be selected for an ensemble scheme.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2016.12.020