Ensembles of Probability Estimation Trees for Customer Churn Prediction
نویسندگان
چکیده
Customer churn prediction is one of the most important elements of any Customer Relationship Management (CRM) strategy. In this study, a number of strategies are investigated to increase the lift of ensemble classification models. In order to increase lift performance, two elements of a number of well-known ensemble strategies are altered: (i) the potential of using probability estimation trees (PETs) instead of standard decision trees as base classifiers is investigated and (ii) a number of alternative fusion rules for the combination of ensemble member’s outputs are proposed and compared. Experiments are conducted for four popular ensemble strategies (Bagging, the Random Subspace Method, SubBag and AdaBoost) on five real-life churn data sets. The results demonstrate the value of using PETs over standard decision trees in order to increase lift. Overall, the effect of the proposed strategies heavily depends on the chosen ensemble algorithm in which they are implemented.
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