COMBINING B&B-BASED HYBRID FEATURE SELECTION AND THE IMBALANCE-ORIENTED MULTIPLE-CLASSIFIER ENSEMBLE FOR IMBALANCED CREDIT RISK ASSESSMENT

作者：Jie Sun, Young‐Chan Lee, Hui Li, Qinghua Huang · 发表于：Technological and Economic Development of Economy · 年份：2015 · DOI：10.3846/20294913.2014.884024 · 被引用次数：19 · 研究领域：Financial Distress and Bankruptcy Prediction、Imbalanced Data Classification Techniques

An ideal model for credit risk assessment is supposed to select important features and process imbalanced data sets in an effective manner. This paper proposes an integrated method that combines B&B (branch and bound)-based hybrid feature selection (BBHFS) with the imbalanceoriented multiple-classifier ensemble (IOMCE) for imbalanced credit risk assessment and uses the support vector machine (SVM) and the multiple discriminant analysis (MDA) as the base predictor. BBHFS is a hybrid feature selection method that integrates the t-test and B&B with the k-fold crossvalidation method to search for a satisfactory feature subset. The IOMCE divides majority samples into several subsets and then combines them with minority samples to construct several training sets for constructing a multiple-classifier ensemble model. We conduct main experiments using a 1:3 imbalanced corporate credit risk data set with continuous features and extended experiments using a 1:5 imbalanced data set with continuous features and a 1:3 imbalanced data set with discrete and nominal features. We combine no feature selection and five feature selection methods (the pure B&B, the factor analysis, the pure t-test, t-test & correlation analysis, and BBHFS) with single-classifier and the IOMCE to construct SVM and MDA models for an empirical comparison. When all features are continuous, the BBHFS-IOMCE method generally outperforms all the other methods. More specifically, BBHFS provides more stable...