SCUT: Multi-Class Imbalanced Data Classification using SMOTE and Cluster-based Undersampling-Reference-Cited by-同舟云学术

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SCUT: Multi-Class Imbalanced Data Classification using SMOTE and Cluster-based Undersampling

Published:2015 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
language:
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Author:

Agrawal Astha¹,Viktor Herna L.¹,Paquet Eric²

Affiliation:

1. University of Ottawa, Canada

2. University of Ottawa and National Research Council of Canada, Canada

Publisher

SCITEPRESS - Science and and Technology Publications

Cited by 41 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. Randomized feature selection based semi-supervised latent Dirichlet allocation for microbiome analysis;Scientific Reports;2024-04-17

2. GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data;Machine Learning;2023-11-20

3. Enhanced SMOTE Strategy for Handling Imbalanced Data in Machine Learning Classification;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

4. Evidence-based adaptive oversampling algorithm for imbalanced classification;Knowledge and Information Systems;2023-09-23

5. Density-Based Clustering to Deal with Highly Imbalanced Data in Multi-Class Problems;Mathematics;2023-09-21

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