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Poly-bagging predictors for classification modelling for credit scoring

Louzada, Francisco; Anacleto Junior, Osvaldo; Candolo, Cecilia and Mazucheli, Josimara (2011). Poly-bagging predictors for classification modelling for credit scoring. Expert Systems with Applications, 38 pp. 12717–12720.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1016/j.eswa.2011.04.059
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Abstract

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement.

Item Type: Journal Article
Copyright Holders: 2011 Elsevier Ltd.
ISSN: 0957-4174
Keywords: credit scoring; bagging; classification tree; combining predictors; logistic regression
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 33030
Depositing User: Emma Howard
Date Deposited: 06 Mar 2012 10:15
Last Modified: 11 Dec 2012 10:08
URI: http://oro.open.ac.uk/id/eprint/33030
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