Cited 1 time in
Assigning different weights to feature values in naive bayes
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lee, C.-H. | - |
| dc.date.accessioned | 2024-08-08T04:00:56Z | - |
| dc.date.available | 2024-08-08T04:00:56Z | - |
| dc.date.issued | 2016 | - |
| dc.identifier.issn | 1865-0929 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/17424 | - |
| dc.description.abstract | Assigning 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.extent | 9 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Springer Verlag | - |
| dc.title | Assigning different weights to feature values in naive bayes | - |
| dc.type | Article | - |
| dc.publisher.location | 독일 | - |
| dc.identifier.doi | 10.1007/978-981-10-2777-2_15 | - |
| dc.identifier.scopusid | 2-s2.0-84989347558 | - |
| dc.identifier.bibliographicCitation | Communications in Computer and Information Science, v.652, pp 171 - 179 | - |
| dc.citation.title | Communications in Computer and Information Science | - |
| dc.citation.volume | 652 | - |
| dc.citation.startPage | 171 | - |
| dc.citation.endPage | 179 | - |
| dc.type.docType | Conference Paper | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.subject.keywordAuthor | Feature selection | - |
| dc.subject.keywordAuthor | Feature weighting | - |
| dc.subject.keywordAuthor | Kullback-Leibler | - |
| dc.subject.keywordAuthor | Naive bayes | - |
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