Study of corporate credit risk prediction based on integrating boosting and random subspace

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

30 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)13871-13878
Journal / PublicationExpert Systems with Applications
Volume38
Issue number11
Publication statusPublished - Oct 2011

Abstract

With the rapid growth and increased competition in credit industry, the corporate credit risk prediction is becoming more important for credit-granting institutions. In this paper, we propose an integrated ensemble approach, called RS-Boosting, which is based on two popular ensemble strategies, i.e.; boosting and random subspace, for corporate credit risk prediction. As there are two different factors encouraging diversity in RS-Boosting, it would be advantageous to get better performance. Two corporate credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed method. Experimental results reveal that RS-Boosting gets the best performance among seven methods, i.e.; logistic regression analysis (LRA), decision tree (DT), artificial neural network (ANN), bagging, boosting and random subspace. All these results illustrate that RS-Boosting can be used as an alternative method for corporate credit risk prediction. © 2011 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Boosting, Corporate credit risk prediction, Ensemble learning, Random subspace