CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification
Authors
Keywords
-
Journal
NEURAL COMPUTING & APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-07-19
DOI
10.1007/s00521-020-05130-z
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deep learning for symbols detection and classification in engineering drawings
- (2020) Eyad Elyan et al. NEURAL NETWORKS
- MFC-GAN: Class-imbalanced dataset classification using Multiple Fake Class Generative Adversarial Network
- (2019) Adamu Ali-Gombe et al. NEUROCOMPUTING
- Neighbourhood-based undersampling approach for handling imbalanced and overlapped data
- (2019) Pattaramon Vuttipittayamongkol et al. INFORMATION SCIENCES
- Boundary-Eliminated Pseudoinverse Linear Discriminant for Imbalanced Problems
- (2018) Yujin Zhu et al. IEEE Transactions on Neural Networks and Learning Systems
- Learning from class-imbalanced data: Review of methods and applications
- (2017) Guo Haixiang et al. EXPERT SYSTEMS WITH APPLICATIONS
- A genetic algorithm approach to optimising random forests applied to class engineered data
- (2017) Eyad Elyan et al. INFORMATION SCIENCES
- Clustering-based undersampling in class-imbalanced data
- (2017) Wei-Chao Lin et al. INFORMATION SCIENCES
- Redundancy-driven modified Tomek-link based undersampling: A solution to class imbalance
- (2017) Debashree Devi et al. PATTERN RECOGNITION LETTERS
- Evolutionary Cluster-Based Synthetic Oversampling Ensemble (ECO-Ensemble) for Imbalance Learning
- (2017) Pin Lim et al. IEEE Transactions on Cybernetics
- A Survey of Predictive Modeling on Imbalanced Domains
- (2016) Paula Branco et al. ACM COMPUTING SURVEYS
- Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy
- (2016) Bartosz Krawczyk et al. APPLIED SOFT COMPUTING
- Associative learning on imbalanced environments: An empirical study
- (2016) L. Cleofas-Sánchez et al. EXPERT SYSTEMS WITH APPLICATIONS
- Cluster-Based Minority Over-Sampling for Imbalanced Datasets
- (2016) Kamthorn PUNTUMAPON et al. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
- DBMUTE: density-based majority under-sampling technique
- (2016) Chumphol Bunkhumpornpat et al. KNOWLEDGE AND INFORMATION SYSTEMS
- Cluster-Based Minority Over-Sampling for Imbalanced Datasets
- (2016) Kamthorn PUNTUMAPON et al. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
- Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction
- (2015) Myoung-Jong Kim et al. EXPERT SYSTEMS WITH APPLICATIONS
- A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients
- (2015) Miriam Seoane Santos et al. JOURNAL OF BIOMEDICAL INFORMATICS
- A fine-grained Random Forests using class decomposition: an application to medical diagnosis
- (2015) Eyad Elyan et al. NEURAL COMPUTING & APPLICATIONS
- MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
- (2012) Sukarna Barua et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
- (2011) Chumphol Bunkhumpornpat et al. APPLIED INTELLIGENCE
- Class imbalance methods for translation initiation site recognition in DNA sequences
- (2011) Nicolás García-Pedrajas et al. KNOWLEDGE-BASED SYSTEMS
- RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
- (2009) Chris Seiffert et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started