Clustering-based undersampling in class-imbalanced data-Reference-Cited by-同舟云学术

Clustering-based undersampling in class-imbalanced data

Published:2017-10 Issue: Volume:409-410 Page:17-26
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Lin Wei-Chao,Tsai Chih-Fong,Hu Ya-Han,Jhang Jing-Shang

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference40 articles.

1. To combat multi-class imbalanced problems by means of over-sampling techniques;Abdi;IEEE Trans. Knowl. Data Eng.,2016

2. New applications of ensembles of classifiers;Barandela;Pattern Anal. Appl.,2003

3. A survey of the behavior of several methods for balancing machine learning training data;Batista;SIGKDD Explor.,2004

4. Classifying imbalanced data sets using similarity based hierarchical decomposition;Bayan;Pattern Recognit.,2015

5. Neighborhood sampling in bagging for imbalanced data;Blaszczynski;Neurocomputing,2015

Cited by 560 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. Optimisation of multiple clustering based undersampling using artificial bee colony: Application to improved detection of obfuscated patterns without adversarial training;Information Sciences;2025-01

2. An extended belief rule-based system with hybrid sampling strategy for imbalanced rule base;Information Sciences;2024-12

3. Class-overlap detection based on heterogeneous clustering ensemble for multi-class imbalance problem;Expert Systems with Applications;2024-12

4. An investigation using resampling techniques and explainable machine learning to minimize fire losses in residential buildings;Journal of Building Engineering;2024-10

5. Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management;Advanced Engineering Informatics;2024-10