SCLS: Multi-label feature selection based on scalable criterion for large label set
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jaesung | - |
dc.contributor.author | Kim, Dae-Won | - |
dc.date.available | 2019-03-08T08:38:11Z | - |
dc.date.issued | 2017-06 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.issn | 1873-5142 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/4348 | - |
dc.description.abstract | Multi-label feature selection involves the selection of relevant features from multi-labeled datasets, resulting in a potential improvement of multi-label learning accuracy. In conventional multi-label feature selection methods, the final feature subset is obtained by identifying the features of high relevance with low redundancy. Thus, accurate score evaluation is a key factor for obtaining an effective feature subset. However, conventional methods suffer from inaccurate conditional relevance evaluation when a large number of labels are involved. As a result, irrelevant features can be a member of the final feature subset, leading to low multi-label learning accuracy. In this paper, we propose a new multi-label feature selection method. Using a scalable relevance evaluation process that evaluates conditional relevance more accurately, the proposed method significantly improves multi-label learning accuracy compared with conventional multi-label feature selection methods. | - |
dc.format.extent | 11 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | SCLS: Multi-label feature selection based on scalable criterion for large label set | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.patcog.2017.01.014 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION, v.66, pp 342 - 352 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000397371800031 | - |
dc.identifier.scopusid | 2-s2.0-85009772926 | - |
dc.citation.endPage | 352 | - |
dc.citation.startPage | 342 | - |
dc.citation.title | PATTERN RECOGNITION | - |
dc.citation.volume | 66 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Multi-label learning | - |
dc.subject.keywordAuthor | Multi-label feature selection | - |
dc.subject.keywordAuthor | Relevance evaluation | - |
dc.subject.keywordAuthor | Conditional relevance | - |
dc.subject.keywordPlus | MUTUAL INFORMATION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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