Graph Convolutional Network|based Feature Selection for High ...

Graph convolutional network-based feature selection for high ...

We present a deep learning-based method—GRAph Convolutional nEtwork feature Selector (GRACES)—to select important features for HDLSS data. GRACES exploits ...

Graph Convolutional Network-based Feature Selection for High ...

Title:Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data ... Abstract:Feature selection is a ...

Graph Convolutional Network-based Feature Selection for High ...

In this paper, we present a deep learning-based method – GRAph Convolutional nEtwork feature Selector (GRACES) – to select important features for HDLSS data. We ...

Graph convolutional network-based feature selection for high ...

Graph convolutional network-based feature selection for high-dimensional and low-sample size data. Can Chen. 1, Scott T. Weiss1, Yang-Yu Liu. 1 ...

[PDF] Graph convolutional network-based feature selection for high ...

A deep learning-based method—GRAph Convolutional nEtwork feature Selector (GRACES)—to select important features for HDLSS data and demonstrates that GRACES ...

(PDF) Graph Convolutional Network-based Feature Selection for ...

Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due ...

Graph convolutional network-based feature selection for high ...

Graph convolutional network-based feature selection for high-dimensional and low-sample size data ; Journal: Bioinformatics, 2023, № 4 ; Publisher: Oxford ...

Graph Convolutional Network-based Feature Selection for High ...

In this paper, we present a deep learning-based method - GRAph Convolutional nEtwork feature Selector (GRACES) - to select important features for HDLSS data. We ...

Stable feature selection utilizing Graph Convolutional Neural ...

Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular ...

Stable feature selection utilizing Graph Convolutional Neural ...

Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast ...

Graph neural network guided by feature selection and centrality ...

based on centrality measures, which are then sent to modify the GNN layer, can lead to better performance across a variety of homophily and heterophily datasets ...

Stability of feature selection utilizing Graph Convolutional Neural ...

model-wide subnetwork consisting of important features. In addition to graph neural network-based approaches, there are methods that use ...

Combinatorial Online High-Order Interactive Feature Selection ...

In the HO-OIFS module, the feature graph convolutional network and pseudo-label dynamic generation mechanism are used to determine the high- ...

Graph convolutional network-based feature selection ... - BibSonomy

Graph convolutional network-based feature selection for high-dimensional and low-sample size data. C. Chen, S. Weiss, and Y. Liu. Bioinform., (April 2023 ).

Explaining graph convolutional network predictions for clinicians ...

Additionally, as the edge weights decrease, the number of neighboring nodes belonging to the same class increases twofold, thereby expanding the feature space.

Stability of feature selection utilizing Graph Convolutional Neural ...

Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior ...

Feature selection: Key to enhance node classification with graph ...

The model consists of two neural networks: classifier and selector. The role of the classifier is to predict the labels of the nodes based on a ...

HiRAND: A novel GCN semi-supervised deep learning-based ...

... based framework for classification and feature selection in drug ... graph and sample graph by graph convolutional network (GCN). The ...

Feature Selection Graph Neural Network for Optimized Node ...

The Feature Selection Graph Neural Network (FSGNN), a straightforward and shallow model, is framed by combining these approaches. Nine standard ...

A Hybrid GCN and Filter‐Based Framework for Channel and ...

A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction ...