Multivariable stream data classification using motifs and their temporal relations
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seo, Sungbo | - |
dc.contributor.author | Kang, Jaewoo | - |
dc.contributor.author | Ryu, Keun Ho | - |
dc.date.accessioned | 2021-09-08T13:22:04Z | - |
dc.date.available | 2021-09-08T13:22:04Z | - |
dc.date.created | 2021-06-11 | - |
dc.date.issued | 2009-09-29 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/119293 | - |
dc.description.abstract | Multivariable stream data is becoming increasingly common as diverse types of sensor devices and networks are deployed. Building accurate classification models for such data has attracted a lot of attention from the research community. Most of the previous works, however, relied on features extracted from individual streams, and did not take into account the dependency relations among the features within and across the streams. In this work, we propose new classification models that exploit temporal relations among features. We showed that consideration of such dependencies does significantly improve the classification accuracy. Another benefit of employing temporal relations is the improved interpretability of the resulting classification models, as the set of temporal relations can be easily translated to a rule using a sequence of inter-dependent events characterizing the class. We evaluated the proposed scheme using different classification models including the Naive Bayesian, TFIDF, and vector distance models. We showed that the proposed model can be a useful addition to the set of existing stream classification algorithms. (C) 2009 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | TIME-SERIES | - |
dc.title | Multivariable stream data classification using motifs and their temporal relations | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kang, Jaewoo | - |
dc.identifier.doi | 10.1016/j.ins.2009.06.036 | - |
dc.identifier.scopusid | 2-s2.0-68049100107 | - |
dc.identifier.wosid | 000269429100003 | - |
dc.identifier.bibliographicCitation | INFORMATION SCIENCES, v.179, no.20, pp.3489 - 3504 | - |
dc.relation.isPartOf | INFORMATION SCIENCES | - |
dc.citation.title | INFORMATION SCIENCES | - |
dc.citation.volume | 179 | - |
dc.citation.number | 20 | - |
dc.citation.startPage | 3489 | - |
dc.citation.endPage | 3504 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | TIME-SERIES | - |
dc.subject.keywordAuthor | Stream data mining | - |
dc.subject.keywordAuthor | Data classification | - |
dc.subject.keywordAuthor | Motifs | - |
dc.subject.keywordAuthor | Temporal relations | - |
dc.subject.keywordAuthor | Multivariable stream | - |
dc.subject.keywordAuthor | Stream data modeling | - |
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