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Domain Adaptation-Based Machine Learning Framework for Customer Churn Prediction Across Varing Distributions
16 references·0 citing·47 second hopsampled
Domain Adaptation-Based Machine Learning Framework for Customer Churn Prediction Across Varing Distributions (2021)Domain Adaptation-Based Machine Learning …Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis (2021)Metamodel-based subset simula…Highly efficient Bayesian updating using metamodels: An adaptive Kriging-based approach (2020)Highly efficient Bayesian upd…Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information (2020)Error Quantification and Cont…Clustering Analysis for Silent Telecom Customers Based on K-means++ (2020)Clustering Analysis for Silen…Real-time high-fidelity reliability updating with equality information using adaptive Kriging (2019)Real-time high-fidelity relia…On confidence intervals for failure probability estimates in Kriging-based reliability analysis (2019)On confidence intervals for f…Customer churn prediction in telecom using machine learning in big data platform (2019)Customer churn prediction in …Comparing to Techniques Used in Customer Churn Analysis (2019)Comparing to Techniques Used …REAK: Reliability analysis through Error rate-based Adaptive Kriging (2018)REAK: Reliability analysis th…ESC: an efficient error-based stopping criterion for kriging-based reliability analysis methods (2018)ESC: an efficient error-based…A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)A new hybrid classification a…Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling (2017)Intelligent churn prediction …Methods for churn prediction in the pre-paid mobile telecommunications industry (2016)Methods for churn prediction …Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models (2012)Reconciling performance and i…New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2011)New insights into churn predi…Customer churn prediction in telecommunications (2011)Customer churn prediction in …Customer satisfaction, customer retention, and market share (1993)The Behavioral Consequences of Service Quality (1996)Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers (2000)Retention Futility: Targeting High-Risk Customers Might be Ineffective (2017)A machine learning framework for customer purchase prediction in the non-contractual setting (2018)Turning telecommunications call details to churn prediction: a data mining approach (2002)Applying data mining to telecom churn management (2005)Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques (2006)Customer churn prediction in the telecommunication sector using a rough set approach (2016)In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions (2017)A Framework for Customer Relationship Management (2001)Valuing Customers (2004)A Survey on Customer Churn Prediction using Machine Learning Techniques (2016)Profit-driven weighted classifier with interpretable ability for customer churn prediction (2024)Knowledge management and data mining for marketing (2001)Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry (2006)Customer churn prediction system: a machine learning approach (2021)Enhancing customer retention in telecom industry with machine learning driven churn prediction (2024)Handling class imbalance in customer churn prediction (2008)Effective ML Techniques to Predict Customer Churn (2021)A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection (2022)Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions (2022)Customer Switching Behavior in Service Industries: An Exploratory Study (1995)Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development (2003)The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention (2005)A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector (2019)COVID‐19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions (2020)Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects (2021)Simulation and the Monte Carlo Method (1981)Simulation and the Monte Carlo Method (2007)Simulation and the Monte Carlo Method (2016)A general failure-pursuing sampling framework for surrogate-based reliability analysis (2018)Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables (2019)Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation (2022)Structural Reliability Methods (1996)Real-time estimation error-guided active learning Kriging method for time-dependent reliability analysis (2019)Adaptive Bayesian support vector regression model for structural reliability analysis (2020)AKSE: A novel adaptive Kriging method combining sampling region scheme and error-based stopping criterion for structural reliability analysis (2021)Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market (2001)Customer Churn Prediction in Telecom Sector Using Machine Learning Techniques (2022)A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners (2024)Estimation of small failure probabilities in high dimensions by subset simulation (2001)An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis (2009)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)Bayesian updating with adaptive, uncertainty-informed subset simulations: High-fidelity updating with multiple observations (2022)BUAK-AIS: Efficient Bayesian Updating with Active learning Kriging-based Adaptive Importance Sampling (2022)Efficient Bayesian model updating for dynamic systems (2023)
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References

  • 2019Customer churn prediction in telecom using machine learning in big data platform576 cited
  • 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees569 cited
  • 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach504 cited
  • 2011Customer churn prediction in telecommunications362 cited
  • 2018ESC: an efficient error-based stopping criterion for kriging-based reliability analysis methods180 cited
  • 2018REAK: Reliability analysis through Error rate-based Adaptive Kriging162 cited
  • 2012Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models73 cited
  • 2016Methods for churn prediction in the pre-paid mobile telecommunications industry70 cited
  • 2017Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling63 cited
  • 2020Highly efficient Bayesian updating using metamodels: An adaptive Kriging-based approach53 cited
  • 2020Clustering Analysis for Silent Telecom Customers Based on K-means++47 cited
  • 2019Real-time high-fidelity reliability updating with equality information using adaptive Kriging42 cited
  • 2019On confidence intervals for failure probability estimates in Kriging-based reliability analysis40 cited
  • 2020

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Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information
38 cited
  • 2019Comparing to Techniques Used in Customer Churn Analysis34 cited
  • 2021Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis27 cited
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