Dimensionality reduction of features for text categorization

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dc.contributor.authorJitpakdee, Parisut
dc.contributor.authorKreesuradej, Worapoj
dc.date.accessioned2026-08-06T09:55:40Z
dc.date.available2026-08-06T09:55:40Z
dc.date.issued2007-12-01
dc.description.abstractThis paper proposes a new technique for dimensionality reduction of features for text categorization. Unlike conventional method, our phrase features are generated based on word sequences of different length (Multigrams) from phrases extracted from whole documents. Then, we utilize Odds ratio (OR) to perform phase feature selection. From preliminary experiments, the proposed techniques show better performance than that of conventional methods.
dc.identifier.citationProceedings of the 3rd IASTED International Conference on Advances in Computer Science and Technology Acst 2007, 506-509, 2007
dc.identifier.other2-s2.0-56149103560
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/1727
dc.sourceProceedings of the 3rd IASTED International Conference on Advances in Computer Science and Technology Acst 2007
dc.subjectData mining
dc.subjectText categorization
dc.subjectText mining
dc.titleDimensionality reduction of features for text categorization
dc.typeConference Paper

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