Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms
作者：
Highlights：
• Propose a new Dynamic Customer Churn Prediction model for Business Intelligence.
• Apply Text Analytics with Metaheuristic Optimization algorithm for Churn Prediction.
• Design a new Chaotic Pigeon Inspired Optimization based Feature Selection technique.
• Employ Parameter Tuned LSTM-SAE model to classify the feature reduced data.
• Validate the classification performance on benchmark churn prediction dataset.
摘要
•Propose a new Dynamic Customer Churn Prediction model for Business Intelligence.•Apply Text Analytics with Metaheuristic Optimization algorithm for Churn Prediction.•Design a new Chaotic Pigeon Inspired Optimization based Feature Selection technique.•Employ Parameter Tuned LSTM-SAE model to classify the feature reduced data.•Validate the classification performance on benchmark churn prediction dataset.
论文关键词：Business intelligence,Churn prediction,Deep learning,Telecommunication industry,Text analytics,Predictive models
论文评审过程：Received 10 April 2021, Revised 1 July 2021, Accepted 22 July 2021, Available online 1 August 2021, Version of Record 1 August 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102706
