Rule-based classification methods, which provide the interpretation of a classification, are very useful in churn prediction. However, most of the rule-based methods are not able to provide the prediction probability which is helpful for evaluating customers. This paper proposes a rule induction based classification algorithm, called CRL. CRL applies several heuristic methods to learn a set of rules, and then uses them to predict the customer potential behaviours. The experiments were carried out to evaluate the proposed method, based on 4 datasets of University of California, Irvine(UCI) and one dataset of telecoms. The experimental results show that CRL can achieve high classification accuracy and outperforms the existing rule-based methods in churn prediction. © 2011 Springer-Verlag.
CITATION STYLE
Huang, Y., Huang, B., & Kechadi, M. T. (2011). A rule-based method for customer churn prediction in telecommunication services. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6634 LNAI, pp. 411–422). Springer Verlag. https://doi.org/10.1007/978-3-642-20841-6_34
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