Overcoming Imbalanced Datasets and Feature Complexity in Fraud Transaction Detection Through Down-Sampling and Dimension Reduction-Reference-Cited by-同舟云学术

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Overcoming Imbalanced Datasets and Feature Complexity in Fraud Transaction Detection Through Down-Sampling and Dimension Reduction

Published:2023-12-20 Issue: Volume: Page:1-11
ISSN:
Container-title:2023 17th International Conference on Open Source Systems and Technologies (ICOSST)
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Author:

Shan Chan Pei¹,Leng Too Ai¹,Ahmad Muneer²,Malik Yasir³,Jaffar Fehmi⁴

Affiliation:

1. Universiti Malaya,Dept. of Information Systems,Malaysia

2. Digby Stuart College, University of Roehampton,United Kingdom

3. Bishops University,Dept. of Computer Science,Canada

4. Université du Québec à Chicoutimi,Département d'informatique et de mathématique,Canada

Funder

Natural Sciences and Engineering Research Council of Canada (NSERC)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10414190/10414192/10505068.pdf?arnumber=10505068

Reference17 articles.

1. Example-dependent cost-sensitive credit cards fraud detection using SMOTE and Bayes minimum risk

2. An Autoencoder Based Model for Detecting Fraudulent Credit Card Transaction

3. Using Variational Auto Encoding in Credit Card Fraud Detection

4. Monetary Transaction Fraud Detection System Based on Machine Learning Strategies

5. Combining unsupervised and supervised learning in credit card fraud detection

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