AMWSPLAdaboost Credit Card Fraud Detection Method Based on Enhanced Base Classifier Diversity
Author:
Affiliation:
1. College of Computer and Communication, Hunan Institute of Engineering, Xiangtan, China
2. College of Computational Science and Electronics, Hunan Institute of Engineering, Xiangtan, China
Funder
Innovation and Entrepreneurship Training Program for College Students in Hunan Province, in 2022
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10168877.pdf?arnumber=10168877
Reference25 articles.
1. Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost
2. A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost
3. Self-paced learning for latent variable models;kumar;Proc 23rd Int Conf Neural Inf Process Syst,2010
4. Credit card fraud classification based on GAN-AdaBoost-DT imbalanced classification algorithm;mo;J Comput Appl,2019
5. Credit Default Risk Analysis Using Machine Learning Algorithms with Hyperparameter Optimization
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