Detecting Fraudulent Insurance Claims Using Random Forests and Synthetic Minority Oversampling Technique-Reference-Cited by-同舟云学术

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Detecting Fraudulent Insurance Claims Using Random Forests and Synthetic Minority Oversampling Technique

Published:2019-11 Issue: Volume: Page:
ISSN:
Container-title:2019 4th International Conference on Information Systems and Computer Networks (ISCON)
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Author:

Harjai Sonakshi,Khatri Sunil Kumar,Singh Gurinder

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9018167/9036148/09036162.pdf?arnumber=9036162

Cited by 13 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. Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector;Applied Artificial Intelligence;2024-05-20

2. Insurance Claim Fraud Detection Using Hidden Naive Bayes;2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT);2024-03-15

3. SONAR Based Under Water Mine Detection Using Machine Learning Algorithms;2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM);2024-02-21

4. Equipment Monitoring In Manufacturing using Bluetooth Low Energy (BLE);2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM);2024-02-21

5. A Cost-Minimization approach to Automobile Insurance Fraud Detection;2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD);2023-11-22

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