A comparative assessment of ensemble learning for credit scoring

Authors:
Gang Wang;Jinxing Hao;Jian Ma;Hongbing Jiang
Affiliations:
School of Management, Hefei University of Technology, Hefei, Anhui 230009, PR China and Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong and School of Economics and Management, BeiHang University, Beijing 100083, PR China;Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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.