Enhancing clinically cardiovascular machine learning model for risk prediction via sample augmentation [PDF]
Frontiers in MedicineBackgroundSmall sample dataset and heterogeneous distributions limit the robustness and implementability of machine learning models for structured clinical data.
Xiaoyu Tang +7 more
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Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN. [PDF]
PLoS ONE, 2021 Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especially ...
ChangHyuk Kwon +3 more
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Hyperspectral estimation of leaf chlorophyll under small-sample conditions via spectral augmentation and weighted ensemble learning [PDF]
Frontiers in Plant ScienceIntroductionUnder small-sample conditions, hyperspectral leaf chlorophyll estimation is affected by high-dimensional collinearity, measurement noise, and cross-source acquisition discrepancies. Existing studies often treat training-distribution expansion
Jiahui Xu +24 more
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Unsupervised Domain Adaptation Algorithm for Time Series Based on Adaptive Contrastive Learning [PDF]
EntropyTime series data find extensive applications in finance, healthcare, and industrial monitoring domains. However, analytical models targeting such data are subject to notable constraints imposed by the rigid independent and identically distributed (IID ...
Huayong Liu, Peng Lin
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Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams
Sensors, 2023 In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue
Yuechen Wang +5 more
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Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [PDF]
Jisuanji kexue, 2022 Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the ...
XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Side-scan sonar (SSS) image sample augmentation plays an important role in improving the effect of deep-learning-based underwater target detection.
Yulin Tang +6 more
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Self-supervised Action Recognition Based on Skeleton Data Augmentation and Double Nearest Neighbor Retrieval [PDF]
Jisuanji kexue, 2023 Traditional self-supervised methods based on skeleton data often take different data augmentation of a sample as positive examples,and the rest of the samples are regarded as negative examples,which makes the ratio of positive and negative samples ...
WU Yushan, XU Zengmin, ZHANG Xuelian, WANG Tao
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A Side-Scan Sonar Image Synthesis Method Based on a Diffusion Model
Journal of Marine Science and Engineering, 2023 The limited number and under-representation of side-scan sonar samples hinders the training of high-performance underwater object detection models. To address this issue, in this paper, we propose a diffusion model-based method to augment side-scan sonar
Zhiwei Yang +4 more
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GIScience & Remote Sensing, 2023 Rapeseed mapping is important for national food security and government regulation of land use. Various methods, including empirical index-based and machine learning-based methods, have been developed to identify rapeseed using remote sensing.
Yunze Zang +8 more
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