Text Classification using Graph Mining-based Feature Extraction
نویسندگان
چکیده
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 expressive document encoding than the more standard bag of words/phrases approach, and consequently gives an improved classification accuracy. Document sets are represented as graph sets to which a weighted graph mining algorithm is applied to extract frequent subgraphs, which are then further processed to produce feature vectors (one per document) for classification. Weighted subgraph mining is used to ensure classification effectiveness and computational efficiency; only the most significant subgraphs are extracted. The approach is validated and evaluated using several popular classification algorithms together with a real world textual data set. The results demonstrate that the approach can outperform existing text classification algorithms on some dataset. When the size of dataset increased, further processing on extracted frequent features is essential. Chuntao Jiang The University of Liverpool, Department of Computer Science, Ashton Building, Ashton Street, Liverpool, L69 3BX, United Kingdom e-mail: [email protected] Frans Coenen The University of Liverpool, Department of Computer Science, Ashton Building, Ashton Street, Liverpool, L69 3BX, United Kingdom e-mail: [email protected] Robert Sanderson The University of Liverpool, Department of Computer Science, Ashton Building, Ashton Street, Liverpool, L69 3BX, United Kingdom e-mail: [email protected] Michele Zito The University of Liverpool, Department of Computer Science, Ashton Building, Ashton Street, Liverpool, L69 3BX, United Kingdom e-mail: [email protected] Chuntao Jiang, Frans Coenen, Robert Sanderson, and Michele Zito
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