期刊名称：International Journal of Intelligent Systems and Applications
印刷版ISSN：2074-904X
电子版ISSN：2074-9058
出版年度：2018
卷号：10
期号：5
页码：71-81
DOI：10.5815/ijisa.2018.05.08
出版社：MECS Publisher
摘要：Customer retention is becoming a key success factor for many business applications due to increasing market competition. Especially telecom companies are facing this challenge with a rapidly increasing number of service providers. Hence there is need to focus on customer churn prediction in order to detect the customers that are likely to churn i.e. switch from one service provider to another. Several data mining techniques are applied for classifying customers into the churn and non-churn category. But churn prediction applications comprise an imbalanced distribution of the dataset. One of the commonly used techniques to handle imbalanced data is re-sampling of data as it is independent of the classifier being used. In this paper, we develop a hybrid re-sampling approach named SOS-BUS by combining well known oversampling technique SMOTE with our novel under-sampling technique. Our methodology aims to focus on the necessary data of majority class and avoid their removal in order to overcome the limitation of random under-sampling. Experimental results show that the proposed approach outperforms the other reference techniques in terms of Area under ROC Curve (AUC).
