Churn Prediction in Telecom Industry using Machine Learning Ensembles with Class Balancing-Reference-Cited by-同舟云学术

Churn Prediction in Telecom Industry using Machine Learning Ensembles with Class Balancing

Published:2021-12-08 Issue: Volume: Page:
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Container-title:2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
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Author:

Chowdhury Abdullahi¹,Kaisar Shahriar²,Rashid Md Mamunur³,Shafin Sakib Shahriar⁴,Kamruzzaman Joarder¹

Affiliation:

1. Federation University Australia,School of Engineering, IT & Physical Sciences,Australia,VIC 3041

2. RMIT University,Department of Information Systems and Business Analytics,Australia,VIC 3001

3. Central Queensland University,School of Engineering & Technology,Rockhampton North,Australia,QLD 4701

4. Islamic University of Technology,Department of Electrical & Electronic Engineering,Gazipur,Bangladesh

Publisher

IEEE

Link

Reference23 articles.

3. Borderline-smote: a new oversampling method in imbalanced data sets learning;han;International Conference on Intelligent Computing,2005

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