Computer Science ›› 2016, Vol. 43 ›› Issue (6): 208-213.doi: 10.11896/j.issn.1002-137X.2016.06.042

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Deep Random Forest for Churn Prediction

YANG Xiao-feng, YAN Jian-feng, LIU Xiao-sheng and YANG Lu   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Churn prediction models help telecom operators identify potential off-network user.Most previous models adopt shallow machine learning algorithms such as logistic regression,decision tree,random forest and neural networks.This paper proposed a novel deep random forest algorithm,which is a multi-layer random forest with layer-wise trai-ning.In terms of telecom operators’ real data,we confirmed that the proposed deep random forest performs better than previous shallow learning algorithms in churn prediction.Moreover,increasing the volume of training data can further improve the performance of deep random forest,which implies that big data make deep models advantageous over shallow models.

Key words: Churn prediction,Deep random forest

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