Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms-Reference-Cited by-同舟云学术

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Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms

Published:2021-04-03 Issue: Volume: Page:1-28
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Singh Amit¹^ORCID,Ranjan Ranjeet Kumar²^ORCID,Tiwari Abhishek³

Affiliation:

1. Indian Computer Emergency Response Team, Ministry of Electronics and Information Technology, New Delhi, India

2. School of Computing, DIT University, Dehradun, India

3. Department of Computer Science, Central University of Haryana, Mahendergarh, India

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Theoretical Computer Science,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2021.1907795

Reference64 articles.

1. A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios

2. An improved algorithm for neural network classification of imbalanced training sets

3. Learning from imbalanced data sets with a Min-Max modular support vector machine

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