Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector-Reference-Cited by-同舟云学术

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Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector

Published:2024-05-20 Issue:1 Volume:38 Page:
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Ming Ruixing¹,Mohamad Osama¹²,Innab Nisreen³,Hanafy Mohamed²^ORCID

Affiliation:

1. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China

2. Department of Statistics, Mathematics, and Insurance, Faculty of Commerce, Assuit University, Asyut, Egypt

3. Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia

Funder

Characteristic Preponderant Discipline of Key Construction Universities in Zhejiang Province

National Natural Science Foundation of China

“Digital +” Discipline Construction and Management Project of Zhejiang Gongshang University

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2024.2355024

Reference69 articles.

1. A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds

2. Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

3. Deep learning versus gradient boosting machine for pan evaporation prediction

4. Unsupervised Feature Selection by Heuristic Search with Provable Bounds on Suboptimality

5. Insurance fraud detection: Evidence from artificial intelligence and machine learning

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