Results 11 to 20 of about 17,625 (278)
Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data [PDF]
Journal of Universal Computer Science, 2020 This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data.
Oscar Camacho-Nieto +2 more
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Uncalibrated Distortions vs Undersampling
Vision Research, 1996 In a recent paper of ours [Hess & Field (1993). Vision Research, 33, 2663-2670], we claim that there was a predictable relationship between position errors and contrast errors for an undersampled system. In this paper we re-state our main points. We feel that the response to that paper by Levi and Klein in the accompanying article does not require us ...
FIELD, DAVID J, HESS, ROBERT F
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Phase-synchronous undersampling in nonlinear spectroscopy [PDF]
Optics Letters, 2018 We introduce the concept of phase-synchronous undersampling in nonlinear spectroscopy. The respective theory is presented and validated experimentally in a phase-modulated quantum beat experiment by sampling high phase modulation frequencies with low laser repetition rates.
Lukas Bruder +2 more
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Journal of Applied Informatics and ComputingHeart disease is one of the leading causes of death in the world with risk factors such as atherosclerosis, high blood pressure, and smoking. Early diagnosis is essential to reduce mortality and improve patients' quality of life. This study evaluates the
Gusti Ayu Putu Febriyanti, Anna Baita
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Trainable Undersampling for Class-Imbalance Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Undersampling has been widely used in the class-imbalance learning area. The main deficiency of most existing undersampling methods is that their data sampling strategies are heuristic-based and independent of the used classifier and evaluation metric. Thus, they may discard informative instances for the classifier during the data sampling.
Minlong Peng +7 more
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The Influence of Radial Undersampling Schemes on Compressed Sensing in Cardiac DTI
Sensors, 2018 Diffusion tensor imaging (DTI) is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Undersampling of k-space data provides an effective way to reduce the amount of data to acquire while maintaining image ...
Jianping Huang +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingSince the minimum antenna area constraint in the spaceborne single-channel synthetic aperture radar (SAR), it's difficult to achieve high-resolution and wide-swath imaging simultaneously.
Zirui Ma +3 more
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Exploiting Prototypical Explanations for Undersampling Imbalanced Datasets [PDF]
, 2022 peer reviewedAmong the reported solutions to the class imbalance issue, the undersampling approaches, which remove instances of insignificant samples from the majority class, are quite prevalent.
Lefebvre, Clément +5 more
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IEEE Access, 2023 The paper proposes an approach for mining imbalanced datasets combining specialized oversampling and undersampling methods. The oversampling part produces a set of non-dominated synthetic examples using two, possibly conflicting, criteria including ...
Joanna Jedrzejowicz, Piotr Jedrzejowicz
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Undersampled Phase Retrieval With Outliers [PDF]
IEEE Transactions on Computational Imaging, 2015 11 pages, 9 ...
Daniel S. Weller +5 more
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