Text classification using graph mining-based feature extraction
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A graph-based approach to document classification is described in this paper. The graph representation offers the advantage that it allows for a much more ...
The results demonstrate that the approach can outperform existing text classification algorithms on some dataset. When the size of dataset increased, further ...
Weighted subgraph mining is used to ensure classification effectiveness and computational efficiency; only the most significant subgraphs are extracted. The ...
This paper presents the graphical representation of textual data using text categorization using K-NN that is K nearest neighbor, which is simple and is having ...
A graph-based approach to document classification is described in this paper. The graph representation offers the advantage that it allows for a much more ...
Chuntao Jiang, Frans Coenen , Robert Sanderson, Michele Zito: Text Classification using Graph Mining-based Feature Extraction. SGAI Conf. 2009: 21-34.
In this paper, a novel feature selection method based on the combination of information gain and FAST algorithm is proposed. In our proposed method, at first, ...
The graph convolutional networks (Kipf and Welling, 2016a) takes a graph as input and learns the relationship between the nodes of interest and their neighbors ...
Jun 30, 2023 · In this paper we propose a novel, graph mining approach for text classification. Our approach is based onthe premise that representative -- ...
A graph-based approach to document classification is described in this paper. The graph representation offers the advantage that it allows for a much more ...