A genetic programming based framework for churn prediction in telecommunication industry

被引：11

作者：

Faris, Hossam ^{[1
]}

Al-Shboul, Bashar ^{[2
]}

Ghatasheh, Nazeeh ^{[2
]}

机构：

[1] King Abdullah II School for Information Technology, The University of Jordan, Amman

[2] The University of Jordan, Amman

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8733卷

关键词：

Churn prediction; Genetic programming; Self organizing maps; Telecommunication;

D O I：

10.1007/978-3-319-11289-3_36

中图分类号：

学科分类号：

摘要：

Customer defection is critically important since it leads to serious business loss. Therefore, investigating methods to identify defecting customers (i.e. churners) has become a priority for telecommunication operators. In this paper, a churn prediction framework is proposed aiming at enhancing the ability to forecast customer churn. The framework combine two heuristic approaches: Self Organizing Maps (SOM) and Genetic Programming (GP). At first, SOM is used to cluster the customers in the dataset, and then remove outliers representing abnormal customer behaviors. After that, GP is used to build an enhanced classification tree. The dataset used for this study contains anonymized real customer information provided by a major local telecom operator in Jordan. Our work shows that using the proposed method surpasses various state-of-the-art classification methods for this particular dataset. © Springer International Publishing Switzerland 2014.

引用

页码：353 / 362

页数：9

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