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Improving Document Classification Using Fine-Grained Weights
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Song, Soo-Hwan | - |
| dc.contributor.author | Lee, Chang-Hwan | - |
| dc.date.accessioned | 2024-08-08T04:00:50Z | - |
| dc.date.available | 2024-08-08T04:00:50Z | - |
| dc.date.issued | 2015 | - |
| dc.identifier.issn | 0302-9743 | - |
| dc.identifier.issn | 1611-3349 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/17382 | - |
| dc.description.abstract | In this paper document classification methods using multinomial naive 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.format.extent | 5 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | SPRINGER-VERLAG BERLIN | - |
| dc.title | Improving Document Classification Using Fine-Grained Weights | - |
| dc.type | Article | - |
| dc.publisher.location | 독일 | - |
| dc.identifier.doi | 10.1007/978-3-319-19066-2_47 | - |
| dc.identifier.scopusid | 2-s2.0-84946401103 | - |
| dc.identifier.wosid | 000363236300047 | - |
| dc.identifier.bibliographicCitation | CURRENT APPROACHES IN APPLIED ARTIFICIAL INTELLIGENCE, v.9101, pp 488 - 492 | - |
| dc.citation.title | CURRENT APPROACHES IN APPLIED ARTIFICIAL INTELLIGENCE | - |
| dc.citation.volume | 9101 | - |
| dc.citation.startPage | 488 | - |
| dc.citation.endPage | 492 | - |
| dc.type.docType | Proceedings Paper | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Computer Science | - |
| dc.relation.journalResearchArea | Robotics | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
| dc.relation.journalWebOfScienceCategory | Robotics | - |
| dc.subject.keywordAuthor | Multinomial naive Bayes | - |
| dc.subject.keywordAuthor | Value weighting | - |
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