A comparative assessment of ensemble learning for credit scoring

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

164 Scopus Citations
View graph of relations

Author(s)

  • Gang Wang
  • Jinxing Hao
  • Jian Ma
  • Hongbing Jiang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)223-230
Journal / PublicationExpert Systems with Applications
Volume38
Issue number1
Publication statusPublished - Jan 2011

Abstract

Both statistical techniques and Artificial Intelligence (AI) techniques have been explored for credit scoring, an important finance activity. Although there are no consistent conclusions on which ones are better, recent studies suggest combining multiple classifiers, i.e., ensemble learning, may have a better performance. In this study, we conduct a comparative assessment of the performance of three popular ensemble methods, i.e., Bagging, Boosting, and Stacking, based on four base learners, i.e., Logistic Regression Analysis (LRA), Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). Experimental results reveal that the three ensemble methods can substantially improve individual base learners. In particular, Bagging performs better than Boosting across all credit datasets. Stacking and Bagging DT in our experiments, get the best performance in terms of average accuracy, type I error and type II error. © 2010 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Bagging, Boosting, Credit scoring, Ensemble learning, Stacking

Citation Format(s)

A comparative assessment of ensemble learning for credit scoring. / Wang, Gang; Hao, Jinxing; Ma, Jian; Jiang, Hongbing.

In: Expert Systems with Applications, Vol. 38, No. 1, 01.2011, p. 223-230.

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