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Conference Paper: Naive bayes classification of uncertain data

TitleNaive bayes classification of uncertain data
Authors
KeywordsNaive bayes model
Uncertain data mining
Issue Date2009
PublisherIEEE, Computer Society.
Citation
The 9th IEEE International Conference on Data Mining (ICDM), Miami, FL., 6-9 December 2009. In Proceedings of the 9th ICDM, 2009, p. 944-949 How to Cite?
AbstractTraditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements, etc. With uncertainty, the value of each data item is represented by a probability distribution function (pdf). In this paper, we propose a novel naive Bayes classification algorithm for uncertain data with a pdf. Our key solution is to extend the class conditional probability estimation in the Bayes model to handle pdf's. Extensive experiments on UCI datasets show that the accuracy of naive Bayes model can be improved by taking into account the uncertainty information. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/125691
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorRen, Jen_HK
dc.contributor.authorLee, SDen_HK
dc.contributor.authorChen, Xen_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorCheng, Ren_HK
dc.contributor.authorCheung, Den_HK
dc.date.accessioned2010-10-31T11:46:19Z-
dc.date.available2010-10-31T11:46:19Z-
dc.date.issued2009en_HK
dc.identifier.citationThe 9th IEEE International Conference on Data Mining (ICDM), Miami, FL., 6-9 December 2009. In Proceedings of the 9th ICDM, 2009, p. 944-949en_HK
dc.identifier.issn1550-4786en_HK
dc.identifier.urihttp://hdl.handle.net/10722/125691-
dc.description.abstractTraditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements, etc. With uncertainty, the value of each data item is represented by a probability distribution function (pdf). In this paper, we propose a novel naive Bayes classification algorithm for uncertain data with a pdf. Our key solution is to extend the class conditional probability estimation in the Bayes model to handle pdf's. Extensive experiments on UCI datasets show that the accuracy of naive Bayes model can be improved by taking into account the uncertainty information. © 2009 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE, Computer Society.-
dc.relation.ispartofProceedings of the IEEE International Conference on Data Mining, ICDM 2009en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectNaive bayes modelen_HK
dc.subjectUncertain data miningen_HK
dc.titleNaive bayes classification of uncertain dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1550-4786&volume=&spage=944&epage=949&date=2009&atitle=Naive+bayes+classification+of+uncertain+data-
dc.identifier.emailKao, B:kao@cs.hku.hken_HK
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_HK
dc.identifier.emailCheung, D:dcheung@cs.hku.hken_HK
dc.identifier.authorityKao, B=rp00123en_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.identifier.authorityCheung, D=rp00101en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDM.2009.90en_HK
dc.identifier.scopuseid_2-s2.0-77951200347en_HK
dc.identifier.hkuros171742en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77951200347&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage944en_HK
dc.identifier.epage949en_HK
dc.identifier.isiWOS:000287216600114-
dc.description.otherThe 9th IEEE International Conference on Data Mining (ICDM), Miami, FL., 6-9 December 2009. In Proceedings of the 9th ICDM, 2009, p. 944-949-
dc.identifier.scopusauthoridRen, J=7403083639en_HK
dc.identifier.scopusauthoridLee, SD=7601400741en_HK
dc.identifier.scopusauthoridChen, X=35811839300en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridCheng, R=7201955416en_HK
dc.identifier.scopusauthoridCheung, D=34567902600en_HK

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