This paper introduces a novel cost sensitive weighted samples approach to a cascade of Graph Neural Networks for learning from imbalanced data in the graph ...
Abstract. This paper introduces a novel cost sensitive weighted samples approach to a cascade of Graph Neural Networks for learning from imbalanced data in ...
This paper introduces a novel cost sensitive weighted samples approach to a cascade of Graph Neural Networks for learning from imbalanced data in the graph ...
In this paper, we will apply a recently proposed connectionist model, namely, the Graph Neural Network, for processing the graph formed by considering each ...
Mar 28, 2023 · In this paper, we are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by creatively combining cost-sensitive learning and graph neural ...
Missing: cascade | Show results with:cascade
This paper takes imbalanced regression as the research proposition for the first time, aiming to develop a framework for health prognosis of mechanical ...
TL;DR: Experiments show that the proposed algorithms, BalanceCascade and EasyEnsemble, have better AUC scores than many existing class-imbalance learning ...
Missing: cascade | Show results with:cascade
A novel cost sensitive weighted samples approach to a cascade of Graph Neural Networks for learning from imbalanced data in the graph structured input ...
Sep 15, 2022 · We present our proposed method by combining three research areas: Feature Selection, node Representation Learning, and Information Diffusion.
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Cascade prediction aims at modeling information diffusion in the network. Most previous methods concentrate on mining either structural or sequential features ...