A Novel Genetic Algorithm Based Method for Building Accurate and Comprehensible Churn Prediction Models
سال انتشار: 1392
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 116
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شناسه ملی سند علمی:
JR_RIEJ-2-4_001
تاریخ نمایه سازی: 15 فروردین 1401
چکیده مقاله:
Customer churn has become a critical problem for all companies in particular for those that are operating in service-based industries such as telecommunication industry. Data mining techniques have been used for constructing churn prediction models. Past research in churn prediction context have mainly focused on the accuracy aspect of the constructed churn models. However, in addition to the accuracy, comprehensibility aspect should be considered in evaluating a churn prediction model. Being comprehensible, a model can reveal the main reasons for customer churn; thereby mangers can use such information for effective decisions making about marketing actions. In this paper, we demonstrate the application of a genetic-algorithm (GA) method for building accurate and comprehensible churn prediction model. The proposed method, GA-based method uses a wrapper based feature selection approach for choosing the best feature subset. The key advantage of this method, is taking into account the comprehensibility measure (measured as the number of rules extracted from C۴.۵ decision tree) in evaluating the performance of a candidate model. The GA-based method is compared to the two filter feature selection methods including Chi-squared based and Correlation based feature selection using two telecommunication churn datasets. The results of experiments indicated that the GA-based method performs better than the two filter methods in terms of both accuracy and comprehensibility
کلیدواژه ها:
نویسندگان
H. Abbasimehr
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
S. Alizadeh
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran