Get started
Keyword

undersampling

Description: Undersampling is a data-processing technique that reduces the number of observations in a dataset, often to address class imbalance in machine learning.
OpenAlex ID: keywords/undersampling
SMOTE: Synthetic Minority Over-sampling Technique
2002 · N. V. Chawla, K. W. Bowyer, et al. · Journal of Artificial Intelligence Research · 32,521
PDF
An Introduction To Compressive Sampling
2008 · E.J. Candes, M.B. Wakin · IEEE Signal Processing Magazine · 10,006
A study of the behavior of several methods for balancing machine learning training data
2004 · Gustavo E. A. P. A. Batista, Ronaldo C. Prati, et al. · ACM SIGKDD Explorations Newsletter · 4,207
A systematic study of the class imbalance problem in convolutional neural networks
2018 · Mateusz Buda, Atsuto Maki, et al. · Neural Networks · 3,055
PDF
Exploratory Undersampling for Class-Imbalance Learning
2008 · Xu-Ying Liu, Jianxin Wu, et al. · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2,475
Differential abundance analysis for microbial marker-gene surveys
2013 · Joseph N Paulson, O Colin Stine, et al. · Nature Methods · 2,432
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
2016 · Lemaitre, Guillaume, Nogueira, Fernando, et al. · arXiv (Cornell University) · 1,877
PDF
Improving the full spectrum fitting method: accurate convolution with Gauss–Hermite functions
2016 · Michele Cappellari · Monthly Notices of the Royal Astronomical Society · 1,326
PDF
Training cost-sensitive neural networks with methods addressing the class imbalance problem
2006 · Zhi-Hua Zhou, Xu-Ying Liu · IEEE Transactions on Knowledge and Data Engineering · 1,325
Evaluating predictive models of species’ distributions: criteria for selecting optimal models
2003 · Robert P Anderson, Daniel Lew, et al. · Ecological Modelling · 1,286
Applying Support Vector Machines to Imbalanced Datasets
2004 · Rehan Akbani, Stephen Kwek, et al. · Lecture notes in computer science · 1,152
SMOTE for high-dimensional class-imbalanced data
2013 · Rok Blagus, Lara Lusa · BMC Bioinformatics · 1,114
PDF
Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT
2006 · Emil Y. Sidky, Chien-Min Kao, et al. · Journal of X-Ray Science and Technology · 1,103
PDF
A Multiple Resampling Method for Learning from Imbalanced Data Sets
2004 · Andrew Estabrooks, Taeho Jo, et al. · Computational Intelligence · 1,014
PDF
SVMs Modeling for Highly Imbalanced Classification
2008 · Yuchun Tang, Yan-Qing Zhang, et al. · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 970
Image subtraction using a space-varying kernel
2000 · C. Alard · Astronomy & Astrophysics Supplement Series · 837
PDF
Clustering-based undersampling in class-imbalanced data
2017 · Wei-Chao Lin, Chih-Fong Tsai, et al. · Information Sciences · 832
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
2003 · Chris Drummond, Robert C. Holte · 786
Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results
2020 · Roweida Mohammed, Jumanah Rawashdeh, et al. · International Conference on Information and Communication Systems (ICICS) · 756
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
2011 · Iain Brown, Christophe Mues · Expert Systems with Applications · 732
PDF
Cluster-based under-sampling approaches for imbalanced data distributions
2008 · Show-Jane Yen, Yue-Shi Lee · Expert Systems with Applications · 732
Phase retrieval from the magnitude of the Fourier transforms of nonperiodic objects
1998 · J. Miao, D. Sayre, et al. · Journal of the Optical Society of America A · 692
Calibrating Probability with Undersampling for Unbalanced Classification
2015 · Andrea Dal Pozzolo, Olivier Caelen, et al. · IEEE Symposium Series on Computational Intelligence (SSCI) · 653
Credit card fraud detection using machine learning techniques: A comparative analysis
2017 · John O. Awoyemi, Adebayo O. Adetunmbi, et al. · 640
Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi‐slice imaging
2005 · Felix A. Breuer, Martin Blaimer, et al. · Magnetic Resonance in Medicine · 633
PDF
Works count: 7,083
Citation count: 172,200
Year
Topic
Type