Predicting non-performing loan of business bank by multiple classifier fusion algorithms

AbstractThis paper uses multiple classifier fusion methods to predict the non-performing loans (NPL) of business bank. Both macroeconomic and bank-specific variables are collected to form the feature set firstly. Based on selected features, the study applies bagging and AdaBoost algorithms, which are described in this paper as two different method of multiple classifier fusion, to build prediction models. To contrast the classification performances, several basic strong classifiers such as decision tree, k nearest neighbors and support vector machine (SVM) are also adopted. In this experiment, non-performing loans data with 96 features and 10415 instances of a business bank is collected. F-mean and the Area under the ROC Curve (AUC) are considered as metrics of classification performances. The results illustrate that multiple classifier fusion algorithms outperform single basic classifier. Furthermore, the AdaBoost method performs much better than bagging method in processing NPL data of business bank.