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Feb 10, 2024 · We devise a lightweight topological augmentation framework called TOBE to mitigate the class-imbalance bias without class rebalancing.
Aug 27, 2023 · We devise a lightweight topological augmentation framework BAT to mitigate the class-imbalance bias without class rebalancing.
In this paper, we study class-imbalanced graph learning from a novel topological perspective. We theoretically reveal that two fundamental topological phenomena ...
Abstract. Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing stud-.
Class-Imbalanced Graph Learning without Class Rebalancing
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May 20, 2024 · Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models.
BAT (BAlanced Topological augmentation) is a lightweight, plug-and-play augmentation technique for class-imbalanced node classification.
Jul 8, 2019 · I am training a neural network on a binary classification problem and my Case (1) and Controls (0) were imbalanced so I oversampled my cases.
Missing: Graph | Show results with:Graph
5 days ago · Learn how to use data augmentation, resampling techniques, and cost-sensitive learning for solving class imbalance in machine learning.
An imbalanced learning method based on graph tran-smote for fraud ...
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Jul 17, 2024 · The tran-smote on graphs (GTS) method for fraud detection is proposed by this study. Structural features of each type of node are deeply mined using a subgraph ...
Aug 15, 2023 · This paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios.