- Tytuł:
- An Efficient Hybrid Classifier Model for Customer Churn Prediction
- Autorzy:
-
Anitha, M. A.
Sherly, K. K. - Powiązania:
- https://bibliotekanauki.pl/articles/2200701.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
customer churn prediction
bag of learners
ANN
SVM
regression
associative classifier
Apriori Algorithm - Opis:
- 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.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2023, 69, 1; 11--18
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł