A model-agnostic framework, which is compatible with general neural networks (GNN), is proposed to deal with class-imbalanced data in fault intelligent ...
A model-agnostic framework towards class-imbalanced fault diagnosis requirement is proposed to systematically alleviate these problems. Four sub-modules, ...
Oct 22, 2024 · [12] proposed a model-agnostic framework based on a data-rebalanced sampler and balanced margin loss for machine intelligent diagnosis with ...
A model-agnostic framework towards class-imbalanced fault diagnosis requirement is proposed to systematically alleviate these problems. Four sub-modules, ...
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Mar 28, 2022 · Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data.
Jun 8, 2022 · Chen, Learning from class-imbalanced data with a model-agnostic framework for machine intelligent diagnosis, Reliability Engineering ...
Mar 28, 2023 · ... Learning from class-imbalanced data with a. model-agnostic framework for machine intelligent diagnosis", Rel. Eng. Syst. Saf., vol. 216,. Dec ...
Supervised contrastive learning (SupCon) is adopted for the first time in imbalanced fault diagnosis to optimize the feature difference between any two classes ...
Sep 2, 2024 · Machine learning and medical diagnostic studies often struggle with the issue of class imbalance in medical datasets, complicating accurate ...
Dec 12, 2020 · The paper found that by artificially creating class imbalance on those data sets, the neural networks are significantly deteriorated. The ROC ...