Results 11 to 20 of about 13,178 (146)
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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Teknika, 2023 Technological developments in the automotive industry have experienced significant progress in recent years. Currently, many electric vehicles are being produced as an environmentally friendly alternative to vehicles.
Naufal Avilandi Poedjimartojo +2 more
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Precise Undersampling Theorems [PDF]
Proceedings of the IEEE, 2010 Undersampling theorems state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest-provided the object in question obeys a sparsity condition, the samples measure appropriate linear combinations of signal values, and we reconstruct with a particular nonlinear procedure.
Donoho, David L., Tanner, Jared
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Information, 2023 Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models.
Tarid Wongvorachan +2 more
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An Efficient CRT Based Algorithm for Frequency Determination from Undersampled Real Waveform
Sensors, 2023 The Chinese Remainder Theorem (CRT) based frequency estimation has been widely studied during the past two decades. It enables one to estimate frequencies by sub-Nyquist sampling rates, which reduces the cost of hardware in a sensor network.
Yao-Wen Zhang +2 more
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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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MPSUBoost: A Modified Particle Stacking Undersampling Boosting Method
IEEE Access, 2022 Class imbalance problems are prevalent in the real world. In such cases, traditional supervised algorithms tend to have difficulty in recognizing minority data because the models are likely to maximize prediction accuracy by simply ignoring minority data.
Sang-Jin Kim, Dong-Joon Lim
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Sparsity/undersampling tradeoffs in anisotropic undersampling, with applications in MR imaging/spectroscopy [PDF]
Information and Inference: A Journal of the IMA, 2018 Abstract We study anisotropic undersampling schemes like those used in multi-dimensional magnetic resonance (MR) spectroscopy and imaging, which sample exhaustively in certain time dimensions and randomly in others. Our analysis shows that anisotropic undersampling schemes are equivalent to certain block-diagonal measurement systems.
Monajemi, Hatef, Donoho, David L.
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PSU: Particle Stacking Undersampling Method for Highly Imbalanced Big Data
IEEE Access, 2020 Imbalanced classes are a common problem in machine learning, and the computational costs required for proper resampling increases with the data size. In this study, a simple and effective undersampling method, named particle stacking undersampling (PSU ...
Yong-Seok Jeon, Dong-Joon Lim
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Millimeter-Wave InSAR Image Reconstruction Approach by Total Variation Regularized Matrix Completion
Remote Sensing, 2018 Millimeter-wave interferometric synthetic aperture radiometer (InSAR) can provide high-resolution observations for many applications by using small antennas to achieve very large synthetic aperture.
Yilong Zhang +4 more
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