A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
Authors
Keywords
-
Journal
MACHINE LEARNING
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-01-06
DOI
10.1007/s10994-022-06296-4
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
- (2022) Damien Dablain et al. IEEE Transactions on Neural Networks and Learning Systems
- Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
- (2022) Lijue Liu et al. BMC Medical Informatics and Decision Making
- Two density-based sampling approaches for imbalanced and overlapping data
- (2022) Sima Mayabadi et al. KNOWLEDGE-BASED SYSTEMS
- Correlation-based Oversampling aided Cost Sensitive Ensemble learning technique for Treatment of Class Imbalance
- (2021) Debashree Devi et al. JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
- Simulated annealing based undersampling (SAUS): a hybrid multi-objective optimization method to tackle class imbalance
- (2021) Venkata Krishnaveni Chennuru et al. APPLIED INTELLIGENCE
- A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection
- (2021) Zhenchuan Li et al. EXPERT SYSTEMS WITH APPLICATIONS
- No Free Lunch in imbalanced learning
- (2021) Nuno Moniz et al. KNOWLEDGE-BASED SYSTEMS
- Early and accurate prediction of diabetics based on FCBF feature selection and SMOTE
- (2021) Amit Kishor et al. International Journal of System Assurance Engineering and Management
- Fuzzy least squares projection twin support vector machines for class imbalance learning
- (2021) M.A. Ganaie et al. APPLIED SOFT COMPUTING
- RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
- (2021) Michał Koziarski et al. MACHINE LEARNING
- LDAS: Local density-based adaptive sampling for imbalanced data classification
- (2021) Yuanting Yan et al. EXPERT SYSTEMS WITH APPLICATIONS
- CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification
- (2020) Eyad Elyan et al. NEURAL COMPUTING & APPLICATIONS
- A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance
- (2019) Dina Elreedy et al. INFORMATION SCIENCES
- Neighbourhood-based undersampling approach for handling imbalanced and overlapped data
- (2019) Pattaramon Vuttipittayamongkol et al. INFORMATION SCIENCES
- Data imbalance in classification: Experimental evaluation
- (2019) Fadi Thabtah et al. INFORMATION SCIENCES
- SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
- (2018) Alberto Fernandez et al. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
- A Systematic Study of Online Class Imbalance Learning With Concept Drift
- (2018) Shuo Wang et al. IEEE Transactions on Neural Networks and Learning Systems
- Adaptive Learning-Based k-Nearest Neighbor Classifiers With Resilience to Class Imbalance
- (2018) Sankha Subhra Mullick et al. IEEE Transactions on Neural Networks and Learning Systems
- A systematic study of the class imbalance problem in convolutional neural networks
- (2018) Mateusz Buda et al. NEURAL NETWORKS
- Learning from class-imbalanced data: Review of methods and applications
- (2017) Guo Haixiang et al. EXPERT SYSTEMS WITH APPLICATIONS
- RWO-Sampling: A random walk over-sampling approach to imbalanced data classification
- (2014) Huaxiang Zhang et al. Information Fusion
- PDFOS: PDF estimation based over-sampling for imbalanced two-class problems
- (2014) Ming Gao et al. NEUROCOMPUTING
- Analysis of sampling techniques for imbalanced data: An n=648 ADNI study
- (2013) Rashmi Dubey et al. NEUROIMAGE
- Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling
- (2010) Julián Luengo et al. SOFT COMPUTING
- Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
- (2009) Salvador García et al. EVOLUTIONARY COMPUTATION
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started