Improving the classification performance on imbalanced data sets via new hybrid parameterisation model-Reference-Cited by-同舟云学术

Improving the classification performance on imbalanced data sets via new hybrid parameterisation model

Published:2021-09 Issue:7 Volume:33 Page:787-797
ISSN:1319-1578
Container-title:Journal of King Saud University - Computer and Information Sciences
language:en
Short-container-title:Journal of King Saud University - Computer and Information Sciences

Author:

Mohamad Masurah,Selamat Ali^ORCID,Subroto Imam Much,Krejcar Ondrej

Funder

Universiti Teknologi Malaysia

Research University Grant

Malaysia Research University Network

Fundamental Research Grant Scheme

Ministry of Education Malaysia

University of Hradec Kralove, FIM, Czech Republic

Publisher

Elsevier BV

Subject

General Computer Science

Reference46 articles.

3. Improved dominance rough set-based classification system;Azar;Neural Comput. Appl.,2016

Cited by 8 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

3. SMOTE-LOF and Borderline-SMOTE Performance to Overcome Imbalanced Data and Outliers on Classification;2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA);2023-12-13