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Assigning different weights to feature values in naive bayes

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dc.contributor.authorLee, C.-H.-
dc.date.accessioned2024-08-08T04:00:56Z-
dc.date.available2024-08-08T04:00:56Z-
dc.date.issued2016-
dc.identifier.issn1865-0929-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/17424-
dc.description.abstractAssigning weights in features has been an important topic in some classification learning algorithms. While the current weighting methods assign a weight to each feature, in this paper, we assign a different weight to the values of each feature. The performance of naive Bayes learning with value-based weighting method is compared with that of some other traditional methods for a number of datasets. © Springer Nature Singapore Pte Ltd. 2016.-
dc.format.extent9-
dc.language영어-
dc.language.isoENG-
dc.publisherSpringer Verlag-
dc.titleAssigning different weights to feature values in naive bayes-
dc.typeArticle-
dc.publisher.location독일-
dc.identifier.doi10.1007/978-981-10-2777-2_15-
dc.identifier.scopusid2-s2.0-84989347558-
dc.identifier.bibliographicCitationCommunications in Computer and Information Science, v.652, pp 171 - 179-
dc.citation.titleCommunications in Computer and Information Science-
dc.citation.volume652-
dc.citation.startPage171-
dc.citation.endPage179-
dc.type.docTypeConference Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorFeature selection-
dc.subject.keywordAuthorFeature weighting-
dc.subject.keywordAuthorKullback-Leibler-
dc.subject.keywordAuthorNaive bayes-
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