Dynamic classifier ensemble model for customer classification with imbalanced class distribution

Customer classification is widely used in customer relationship management including churn prediction, credit scoring, cross-selling and so on. In customer classification, an important yet challenging problem is the imbalance of data distribution. In this paper, we combine ensemble learning with cost-sensitive learning, and propose a dynamic classifier ensemble method for imbalanced data (DCEID). For each test customer, it can adaptively select out the more appropriate one from the two kinds of dynamic ensemble approach: dynamic classifier selection (DCS) and dynamic ensemble selection (DES). Meanwhile, new cost-sensitive selection criteria for DCS and DES are constructed respectively to improve the classification ability for imbalanced data. We apply this method to a credit scoring dataset in UCI and a real churn prediction dataset from a telecommunication company. The experimental results show that the classification performance of DCEID is not only better than some static ensemble methods such as weighted random forests and improved balanced random forests, but also better than the existing DCS and DES strategies.