Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning

Han, Hui; Wang, Wen-Yuan; Mao, Bing-Huan

doi:10.1007/11538059_91

Cited by 4,331Imbalanced Data Classification TechniquesMachine Learning and Data ClassificationData Stream Mining Techniques

Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning

Han, Hui, Wang, Wen-Yuan, Mao, Bing-Huan·2005·Lecture notes in computer science

DOI 10.1007/11538059_91

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Han et al. introduced the value of imbalanced datasets as well as their broad application areas in data mining.
“Han et al. introduced the value of imbalanced datasets as well as their broad application areas in data mining [127].”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [127]
Two new minorities were proposed by this method using borderline-SMOTE 1 and borderline-SMOTE 2 over the sampling method.
“Two new minorities were proposed by this method using borderline-SMOTE 1 and borderline-SMOTE 2 over the sampling method.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [127]
The synthetic minority over-sampling technique (SMOTE) is one of the oversampling techniques used to address imbalance.
“To address this issue, the synthetic minority over-sampling technique (SMOTE) is one of the oversampling tech-niques used.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [127]

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Authors: Han, H., Wang, W.-Y., & Mao, B.-H.
Affiliations: Tsinghua University (CN)
ORCID: Han, Hui
Venue: Lecture notes in computer science
Publisher: Springer Berlin Heidelberg
Published: 2005
Pages: 878-887
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/11538059_91
OpenAlex: W2132791018
Cited by: 4,331
References: 28
Citations by year: 2026: 282 · 2025: 516 · 2024: 575 · 2023: 555 · 2022: 552 · 2021: 509 · 2020: 354 · 2019: 270 · 2018: 162 · 2017: 128 · 2016: 87 · 2015: 72 · 2014: 72 · 2013: 49 · 2012: 44
FWCI: 5.47
Type: conference paper
Language: en
Open access: closed
Locations: Lecture notes in computer science (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
Keywords: class imbalance, oversampling, SMOTE, Borderline-SMOTE, minority class, synthetic minority samples

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Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning

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