Application of Feature Extraction Method in Customer Churn Prediction Based on Random Forest and Transduction

With the development of telecom business, customer churn prediction becomes more and more important. An outstanding issue in customer churn prediction is high dimensional problem. Curse of dimensionality will easily occur if effective feature extraction is not applied during modeling. Among the most popular feature extraction approaches, principal component analysis (PCA) method based on induction learning usually loses certain information contained in the features of test data. Different with induction learning method, a new method based on Random Forest and Transduction is developed in this paper. Experiments results on the UCI data show that compared to PCA the proposed method makes full use of the information contained in training samples and test data and improves the performance of learning machine effectively with fewer features. Application of this new method on customer churn prediction also shows it's efficient.

• Type: Journal
• Year: 2010
• Where: JCIT
• Authors: Yihui Qiu, Hong Li
• Keywords: Customer Churn Prediction, Feature Extraction, JCIT 2010, Test Data