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Improving document classification using fine-grained weights

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dc.contributor.authorSong, Soo-Hwanko
dc.contributor.authorLee, Chang-Hwanko
dc.date.accessioned2020-03-19T05:23:02Z-
dc.date.available2020-03-19T05:23:02Z-
dc.date.created2020-02-18-
dc.date.issued2015-06-
dc.identifier.citation28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, pp.488 - 492-
dc.identifier.urihttp://hdl.handle.net/10203/273144-
dc.description.abstractIn this paper document classification methods using multinomial na¨ıve Bayes are improved in a number of ways. We use the value weighting method, a new fine-grained weighting method, to calculate the weights of the feature values. Our experiments show that the proposed approach outperforms other state-of-the-art methods.-
dc.languageEnglish-
dc.publisherSpringer Verlag-
dc.titleImproving document classification using fine-grained weights-
dc.typeConference-
dc.identifier.wosid000363236300047-
dc.identifier.scopusid2-s2.0-84946401103-
dc.type.rimsCONF-
dc.citation.beginningpage488-
dc.citation.endingpage492-
dc.citation.publicationname28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015-
dc.identifier.conferencecountryKO-
dc.identifier.conferencelocationSeoul-
dc.identifier.doi10.1007/978-3-319-19066-2_47-
dc.contributor.nonIdAuthorLee, Chang-Hwan-
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