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Stop Oversampling for Class Imbalance Learning: A Review
IEEE Access, 2022 For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is,
Ahmad S Tarawneh +2 more
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IEEE Access, 2022 Imbalanced datasets are pervasive in classification tasks and would cause degradation of the performance of classifiers in predicting minority samples. Oversampling is effective in resolving the class imbalance problem.
Changhui Liu, Sun Jin, Donghong Wang
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Oversampling for the Multiscale Finite Element Method [PDF]
Multiscale Modeling and Simulation, 2013 This paper reviews standard oversampling strategies as performed in the Multiscale Finite Element Method (MsFEM). Common to those approaches is that the oversampling is performed in the full space restricted to a patch but including coarse finite element functions.
Patrick Henning, Daniel Peterseim
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Selective oversampling approach for strongly imbalanced data [PDF]
PeerJ Computer Science, 2021 Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling.
Peter Gnip +2 more
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Oversampled Adaptive Sensing [PDF]
2018 Information Theory and Applications Workshop (ITA), 2018 We develop a Bayesian framework for sensing which adapts the sensing time and/or basis functions to the instantaneous sensing quality measured in terms of the expected posterior mean-squared error. For sparse Gaussian sources a significant reduction in average sensing time and/or mean-squared error is achieved in comparison to non-adaptive sensing. For
Ralf R. Müller +2 more
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The oversampling phasing method [PDF]
Acta Crystallographica Section D Biological Crystallography, 2000 Sampling the diffraction pattern of a finite specimen more finely than the Nyquist frequency (the inverse of the size of the diffracting specimen) corresponds to surrounding the electron density of the specimen with a no-density region. When the no-density region is bigger than the electron-density region, sufficient information is recorded so that the
J, Miao, J, Kirz, D, Sayre
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Oversampled filter banks [PDF]
IEEE Transactions on Signal Processing, 1998 Perfect reconstruction oversampled filter banks are equivalent to a particular class of frames in l/sup 2/(Z). These frames are the subject of this paper. First, the necessary and sufficient conditions of a filter bank for implementing a frame or a tight frame expansion are established, as well as a necessary and sufficient condition for perfect ...
Zoran Cvetkovic, Martin Vetterli
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Engineering Reports, 2021 This survey paper focuses on one of the current primary issues challenging data mining researchers experimenting on real‐world datasets. The problem is that of imbalanced class distribution that generates a bias toward the majority class due to ...
Dr. Seba Susan, Amitesh Kumar
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FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes
IEEE Access, 2021 With the increased use of machine learning algorithms to make decisions which impact people’s lives, it is of extreme importance to ensure that predictions do not prejudice subgroups of the population with respect to sensitive attributes such as ...
Teresa Salazar +3 more
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Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
Applied Sciences, 2021 The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages ...
Khin Yadanar Win +3 more
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