Customer Personality Analysis for Churn Prediction Using Hybrid Ensemble Models and Class Balancing Techniques-Reference-Cited by-同舟云学术

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Customer Personality Analysis for Churn Prediction Using Hybrid Ensemble Models and Class Balancing Techniques

Published:2024 Issue: Volume:12 Page:1865-1879
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ahmad Noman¹^ORCID,Awan Mazhar Javed¹^ORCID,Nobanee Haitham²^ORCID,Zain Azlan Mohd³^ORCID,Naseem Ansar¹,Mahmoud Amena⁴^ORCID

Affiliation:

1. School of System and Technology, University of Management and Technology, Lahore, Pakistan

2. College of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates

3. Faculty of Computing, University Teknologi Malaysia, Skudai, Johor, Malaysia

4. Computer Science Department, Faculty of Computers and Information, Kafrelsheikh University, Kafr El-Sheikh, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10322869.pdf?arnumber=10322869

Reference28 articles.

1. The Role of Identity Salience in the Effects of Corporate Social Responsibility on Consumer Behavior

2. A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection

3. Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

4. Fake profile recognition using big data analytics in social media platforms

5. Evidence-based policymaking: promise, challenges and opportunities for accounting and financial markets research

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Customer Analytics: A Comprehensive Framework for Effective Churn Prediction;International Journal of Advanced Research in Science, Communication and Technology;2024-03-16

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