RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification-Reference-Cited by-同舟云学术

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

Published:2023-03 Issue:2 Volume:60 Page:103235
ISSN:0306-4573
Container-title:Information Processing & Management
language:en
Short-container-title:Information Processing & Management

Author:

Ding Hongwei^ORCID,Sun Yu,Wang Zhenyu,Huang Nana,Shen Zhidong,Cui Xiaohui

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference56 articles.

1. To combat multi-class imbalanced problems by means of over-sampling techniques;Abdi;IEEE Transactions on Knowledge and Data Engineering,2015

2. Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework;Alcalá-Fdez;Journal of Multiple-Valued Logic and Soft Computing,2011

3. Wasserstein generative adversarial networks;Arjovsky,2017

4. LoRAS: An oversampling approach for imbalanced datasets;Bej;Machine Learning,2021

5. Hellinger net: A hybrid imbalance learning model to improve software defect prediction;Chakraborty;IEEE Transactions on Reliability,2020

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Generative Adversarial Networks for text-to-face synthesis & generation: A quantitative–qualitative analysis of Natural Language Processing encoders for Spanish;Information Processing & Management;2024-05

2. Surface defect detection methods for industrial products with imbalanced samples: A review of progress in the 2020s;Engineering Applications of Artificial Intelligence;2024-04

3. Supervised contrastive representation learning with tree-structured parzen estimator Bayesian optimization for imbalanced tabular data;Expert Systems with Applications;2024-03

4. A hybrid multi-criteria meta-learner based classifier for imbalanced data;Knowledge-Based Systems;2024-02

5. An imbalanced contrastive classification method via similarity comparison within sample-neighbors with adaptive generation coefficient;Information Sciences;2024-02