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- W3199475782 abstract "Customer churn is a common problem within customer service oriented industries and the companies have been always trying to identify the possible churned customers for taking necessary steps to prevent churn. In this paper, data mining technique based a hybrid feature selection-classification model is proposed that can be used to predict which customers are probable to churn. The proposed model works in two phases. In first phase, decision tree, K-prototype clustering and apriori algorithm based a new feature selection technique have been used to form reduced dataset via selection of relevant features. In second phase, an ensemble classifier is formed via combining KNN, Naive Bayes, SVM, Decision tree, and Logistic regression to predict churned customers. The proposed model is applied on a telecom service customer churn dataset. From the experimental results, it can be said that the proposed model gives better results than other existing methods." @default.
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- W3199475782 date "2021-01-01" @default.
- W3199475782 modified "2023-09-25" @default.
- W3199475782 title "A New Hybrid Feature Selection-Classification Method to Identify Churned Customers" @default.
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- W3199475782 doi "https://doi.org/10.1007/978-981-16-0275-7_16" @default.
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