Semi-supervised ensemble based on metacost model for customer churn prediction

Customer churn prediction is an important content of customer relationship management (CRM). In many real customer churn prediction modeling, the class distribution is highly imbalanced, so that the performance of model is poor and it's difficult to achieve satisfactory results. At the same time, in reality, there are only a small number of labeled samples, and a large number of them are unlabeled, which cause a lot of waste of useful information. In order to solve the two problems above, this study combines the technologies of meta cost-sensitive learning, semi-supervised learning and ensemble method of Bagging, and proposes semi-supervised ensemble based on metacost model (SSEM) for customer churn prediction. This model mainly includes the following three stages:1) Metacost method is used to modify the label of initial labeled training set L, a new training set Lm is obtained, then Lm is randomly divided into model training set Ltr and model verification set Va; 2) Va is used to select three base classifiers with the highest classification accuracy, then these classifiers cooperate to selectively label some samples from unlabeled data set U, which are added into Ltr; 3) N base classifiers are trained on the new model training set Ltr, then using them to classify samples in test set, and the final classification results are obtained by integration. The empirical analysis is conducted in two customer churn prediction datasets, and the results show that the performance of SSEM model is superior to the common used supervised ensemble models and the semi-supervised ensemble models. © 2021, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.