Comparison of Novel Optimized Random Forest Technique and Support Vector Machine for Fraudulent activities in credit card Detection with Improved Precision-Reference-Cited by-同舟云学术

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Comparison of Novel Optimized Random Forest Technique and Support Vector Machine for Fraudulent activities in credit card Detection with Improved Precision

Published:2023-04-06 Issue: Volume: Page:
ISSN:
Container-title:2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)
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Author:

Baig M. ShahidSaif Ali¹,Jaisharma K.¹

Affiliation:

1. Saveetha University,Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences,Department of Computer Science and Engineering,Chennai,Tamilnadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10142230/10142223/10142322.pdf?arnumber=10142322

Reference18 articles.

1. Bank Fraud Detection Using SVM;gyamfi;2018 IEEE 9th AIEMCON,2018

2. Application of GA Feature Selection on NB, RF and SVM for Fraudulent activities in credit card Detection;s;2020 IDASA,2020

3. A Comparative Study of Fraudulent activities in credit card Detection Using the Combination of ML Techniques with Data Imbalance Solution;a;2021 2nd ICDS,2021

4. Fraudulent activities in credit card Detection System based on Operational & Transaction features using SVM and Random Forest Classifiers;s;2021 2nd ICCAKM,2021

5. Relative Analysis of ML Algorithm QDA, LR and SVM for Fraudulent activities in credit card Detection Dataset;n;I-SMAC 2020,2020

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