Handling imbalanced data in churn prediction using ADASYN and backpropagation algorithm

Annisa Aditsania · Adiwijaya Adiwijaya · Aldo Lionel Saonard

39 citations0/0 files storedInternational Conference on Science in Information Technology

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Customer churn and segmentation Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

Customers are important assets for an industry, including telecommunication industry. Customer churn will result in lost revenue. For that reason, a classification model that can predict the customer churn is needed. One of the obstacles to develop the classification model is the great difference between number of churn and non-churn customers. The non-churn data dominates the entire customer data. This will make the churn data unfamiliar to the system. ADASYN (Adaptive Synthetic Sampling) is one of the oversampling methods that can be used to solve the class imbalanced problem. ADASYN is algorithm improvement from SMOTE (Synthetic Minority Over-sampling) algorithm [1]. Meanwhile, in this research the backpropagation algorithm is chosen as classification model. The result of the study with proposed scheme shows the testing accuracy 96,31% and testing F1-Score 0.4607.

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References

2008Handling class imbalance in customer churn prediction 2008Customer churn prediction using improved balanced random forests 2014A Proposed Churn Prediction Model 2019A Proposed Churn Prediction Model 2014Handling imbalanced data in customer churn prediction using combined sampling and weighted random forest

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2024Customer churn prediction model based on hybrid neural networks 2020Müşteri Kaybı Tahmininde Sınıf Dengesizliği Problemi

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