A self‐adaptive synthetic over‐sampling technique for imbalanced classification
Author:
Affiliation:
1. School of Computing and CommunicationsLancaster University Lancaster UK
2. Lancaster Intelligent, Robotic and Autonomous Systems CentreLancaster University Lancaster UK
3. Honorary ProfessorTechnical University Sofia Bulgaria
Publisher
Wiley
Subject
Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22230
Reference36 articles.
1. SoaresE AngelovP.Fair‐by‐design explainable models for prediction of recidivism.ArXiv: 191002043.2019.
2. A synthetic neighborhood generation based ensemble learning for the imbalanced data classification
3. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature
4. Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data
5. Imbalance fault detection based on the integrated analysis strategy for variable-speed wind turbines
Cited by 45 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. Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management;Advanced Engineering Informatics;2024-10
2. Automatic Classification Framework for Neonatal Seizure Using Wavelet Scattering Transform and Nearest Component Analysis;IRBM;2024-08
3. Enhancing fraud detection efficiency in mobile transactions through the integration of bidirectional 3d Quasi-Recurrent Neural network and blockchain technologies;Expert Systems with Applications;2024-08
4. An effective two-stage training scheme for boundary decision of imbalanced samples;International Journal of Machine Learning and Cybernetics;2024-06-14
5. Enhancing and improving the performance of imbalanced class data using novel GBO and SSG: A comparative analysis;Neural Networks;2024-05
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3