The Impact of SMOTE and ADASYN on Random Forest and Advanced Gradient Boosting Techniques in Telecom Customer Churn Prediction-Reference-Cited by-同舟云学术

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The Impact of SMOTE and ADASYN on Random Forest and Advanced Gradient Boosting Techniques in Telecom Customer Churn Prediction

Published:2024-04-24 Issue: Volume:77 Page:202-209
ISSN:
Container-title:2024 10th International Conference on Web Research (ICWR)
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Author:

Imani Mehdi¹,Ghaderpour Zahra²,Joudaki Majid³,Beikmohammadi Ali⁴

Affiliation:

1. Stockholm University,Department of Computer and System Sciences,Stockholm,Sweden,10691

2. Ayatollah Boroujerdi University,Electrical and Computer Engineering,Boroujerd,Iran

3. University of Kashan,Electrical and Computer Engineering,Kashan,Iran

4. Stockholm University,Department of Computer and Systems Sciences,Stockholm,Sweden,SE-16455

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10533315/10533316/10533320.pdf?arnumber=10533320

Reference29 articles.

1. Ensemble Learning for AI Developers

2. Improved customer choice predictions using ensemble methods

3. A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector

4. Customer churn prediction system: a machine learning approach

5. An overview of classification and its algorithms;Mazhari

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