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English
صفحه اصلی
/
یازدهمین کنفرانس بین المللی فناوری اطلاعات و دانش
Effective Classifier for Predicting Churn in Payment Terminals Using RFM model and Deep Neural Network
نویسندگان :
Mahila Dadfarnia
1
Ali Alemi Matinpour
2
Monireh Abdoos
3
1- دانشگاه یزد
2- تربیت مدرس تهران
3- تربیت مدرس تهران
کلمات کلیدی :
Payment terminals, Churn prediction, RFM (recency, frequency and monetary), DNN (Deep Neural Network), Genetic Algorithms
چکیده :
In recent years, there is remarkable growing concern for marketing team to retain their customers. This can be achieved by predicting accurately ahead of time, whether a terminal for buying is valuable in the foreseeable future or not. This paper presents the application of Deep Neural Network in the issue of classifying the payment terminals in different branches of Parsian bank specifically. The paper uses real data for classifying various payment terminals in 6 classes of terminal by a 5 layer deep neural network and RFM model. The empirical results reveal that utilizing the deep network generate significantly better accuracy in comparison with other popular methods
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.8.0
