Research of indicator system in customer churn prediction for telecom industry

Abstract

Curse of dimensionality will occur if effective dimensionality reduction method is not applied in machine learning, especially in the telecom field. The existing researches on customer churn are still lack of a set of scientific, system theory and method and the single models methods for customer churn prediction also are unable to completely meet application needs. Therefore, it is important for theoretical and practical contribution to explore and study the customer churn prediction. Based on the substantive characteristics of customer churn in telecommunication, the indicator system of customer churn in telecommunication are studied in this paper. Firstly, we proposed a feature selection method based on pruning technique, which is called feature selection method based on orientation ordering pruning Method (OOPM). According to this algorithm attribute selection problem can be replaced by the pruning question of classifier combination and we structure an indicator system of customer churn. Secondly, in order to explore high-order statistical information in the properties, a feature extraction method based on Random Forest and Transduction (FE_RF&T) is proposed to extract multiple features from customer data. Experiments on the real data of telecom enterprise show that the feature selection method based on OOPM has more advantages than the feature selection method based on Random Forest, and compared to the PCA method the FE_RF&T method improves the performance of learning machine effectively.