A Fuzzy Rule-based Learning Algorithm for Customer Churn Prediction
|Title:||A Fuzzy Rule-based Learning Algorithm for Customer Churn Prediction||Authors:||Huang, Bingquan
Chen, Chong Cheng
|Permanent link:||http://hdl.handle.net/10197/7856||Date:||17-Jul-2016||Abstract:||Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However most of rulebased learning algorithms designed with the assumption of well-balanced datasets, may provide unacceptable prediction results. This paper introduces a Fuzzy Association Rule-based Classification Learning Algorithm for customer churn prediction. The proposed algorithm adapts CAIM discretization algorithm to obtain fuzzy partitions, then searches a set of rules using an assessment method. The experiments were carried out to validate the proposed approach using the customer services dataset of Telecom. The experimental results show that the proposed approach can achieve acceptable prediction accuracy and efficient for churn prediction.||Funding Details:||European Commission - Seventh Framework Programme (FP7)||Type of material:||Conference Publication||Publisher:||Springer||Copyright (published version):||2016 Springer||Keywords:||Optimisation & Decision Analytics; Fuzzy rules; Churn prediction; Rule-based classification||DOI:||10.1007/978-3-319-41561-1_14||Language:||en||Status of Item:||Peer reviewed||Is part of:||Perner, P. (ed.). Proceedings of the 16th Industrial Conference on Data Mining (ICDM 2016), New York, United States, 13-17 July 2016||Conference Details:||16th Industrial Conference on Data Mining (ICDM 2016), New York, United States, 13-17 July 2016|
|Appears in Collections:||Computer Science Research Collection|
Insight Research Collection
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