Results 51 to 60 of about 13,178 (146)
MathematicsUnbalanced data can have an impact on the machine learning (ML) algorithms that build predictive models. This manuscript studies the influence of oversampling and undersampling strategies on the learning of the Bayesian classification models that predict
Franklin Parrales-Bravo +6 more
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Deep learning for undersampled MRI reconstruction
Physics in Medicine & Biology, 2018 This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting ...
Chang Min Hyun +4 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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The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling
IEEE Access, 2020 In this paper, we propose a new metric to measure goodness-of-fit for classifiers: the Real World Cost function. This metric factors in information about a real world problem, such as financial impact, that other measures like accuracy or F1 do not. This
Yaoshiang Ho, Samuel Wookey
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An oversampling-undersampling strategy for large-scale data linkage
Frontiers in Big DataEffective record linkage in big data, particularly in imbalanced datasets, is a critical yet highly challenging task due to the inherent complexity involved.
Hossein Hassani +4 more
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Combining Undersampled Dithered Images
Publications of the Astronomical Society of the Pacific, 1999 Undersampled images, such as those produced by the HST WFPC-2, misrepresent fine-scale structure intrinsic to the astronomical sources being imaged. Analyzing such images is difficult on scales close to their resolution limits and may produce erroneous results.
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Citation: 'undersampling' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.08297 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for
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Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
Applied SciencesThe improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical ...
Duygu Yilmaz Eroglu, Mestan Sahin Pir
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On the relative importance of the hot stove effect and the tendency to rely on small samples
Judgment and Decision Making, 2009 Experiments have suggested that decisions from experience differ from decisions from description. In experience-based decisions, the decision makers often fail to maximise their payoffs. Previous authors have ascribed the effect of underweighting of rare
Takemi Fujikawa
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Reviving Undersampling for Long-Tailed Learning
Pattern RecognitionThe training datasets used in long-tailed recognition are extremely unbalanced, resulting in significant variation in per-class accuracy across categories. Prior works mostly used average accuracy to evaluate their algorithms, which easily ignores those worst-performing categories.
Hao Yu, Yingxiao Du, Jianxin Wu
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