An Efficient Hybrid Classifier Model for Customer Churn Prediction

• Autorzy: Anitha M. A. , Sherly K. K.

Identyfikatory

DOI

10.24425/ijet.2023.144325

• Języki publikacji: EN

Abstrakty

EN

Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance. Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy. Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall.

Słowa kluczowe

EN

customer churn prediction; bag of learners; ANN; SVM; regression; associative classifier; Apriori Algorithm

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2023
• Tom: Vol. 69, No. 1
• Strony: 11--18
• Opis fizyczny: Bibliogr. 30 poz., tab., wykr.

Twórcy

autor

Anitha M. A.

• Faculty of Computer Science and Engineering, College of Engineering Cherthala, Alappuzha, Kerala, India

autor

Sherly K. K.

• Information Technology Department, Rajagiri School of Engineering & Technology Kochi-682039, Kerala, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).