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Improved term selection algorithm based on variance in text categorization

Ran Li, Xianjiu Guo
2013 Proceedings of the 2nd International Conference On Systems Engineering and Modeling   unpublished

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This article improves the algorithm of term weighting in automated text classification. The traditional TFIDF algorithm is a common method that is used to measure term weighting in text classification.However, the algorithm does not take the distribution of terms in inter-class. In order to solve the problem, variance which describes the distribution of terms in inter-class and intra-class is used to revise TFIDF algorithm. This article mainly researched about the construction of LFHW term sets
more » ... and new approaches to term weighting, These new approaches are also applied to the hierarchical classification system.Compared with traditional TFIDF algorithm ,the results of simulation experiment have demonstrated that the improved TFIDF algorithm can get better classification results. Proceedings of the 2nd International Conference On Systems Engineering and Modeling Published by Atlantis Press,
doi:10.2991/icsem.2013.157 fatcat:ugcgbfxudjb6ldbu5z4nirzjgm
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Ran Li, Xianjiu Guo. "Improved term selection algorithm based on variance in text categorization." Proceedings of the 2nd International Conference On Systems Engineering and Modeling (2013)