CTGAN-MOS: Conditional Generative Adversarial Network Based Minority-Class-Augmented Oversampling Scheme for Imbalanced Problems-Reference-Cited by-同舟云学术

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CTGAN-MOS: Conditional Generative Adversarial Network Based Minority-Class-Augmented Oversampling Scheme for Imbalanced Problems

Published:2023 Issue: Volume:11 Page:85878-85899
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Majeed Abdul¹^ORCID,Hwang Seong Oun¹^ORCID

Affiliation:

1. Department of Computer Engineering, Gachon University, Seongnam, South Korea

Funder

Institute of Information & Communications Technology Planning & Evaluation

Korean Government [Ministry of Science and ICT (MSIT)]

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/ielx7/6287639/10005208/10214234.pdf?arnumber=10214234

Reference61 articles.

1. Class-overlap undersampling based on Schur decomposition for Class-imbalance problems

2. A Python library to check the level of anonymity of a dataset;díaz;Data Science Journal,2022

3. TSK fuzzy system fusion at sensitivity-ensemble-level for imbalanced data classification

4. UCI repository of machine learning databases;murphy,1992

5. Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

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