Customer churn prediction using improved balanced random forests

By Yaya Xie, Eric Wai Ting Ngai, Weiyun Ying. Expert systems with applications, 2009

416 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Mining data with random forests: A survey and results of new tests (2011, neutral): “[45,46] Data: from a Chinese b”
Application of machine learning to construction injury prediction (2016, background): “[6], forecast air traf ﬁ c delays [56], analyze the risk of mortgage prepayment [47], determine the likelihood that a customer will cease doing business with a company [75], predict horse race outcomes [77], and to evaluate the likelihood of being elected to the baseball hall of fame [33].”
Data mining for credit card fraud: A comparative study (2011, background): “Random forests have been applied in recent years across varied domains from predicting customer churn [51], image classification, to various bio-medical problems.”
Churn in Social Networks (2010, background): “Churn has been analysed in a wide range of industries: most widely in the telcom sector [44, 50, 41, 66, 17, 32, 13, 65, 30], but also in the field of retail business [12], banking [69], Internet service providers [31], service industries [53], P2P networks [28] and online games [34].”