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Conference Paper: Decision trees for uncertain data

TitleDecision trees for uncertain data
Authors
KeywordsClassification
Data mining
Decision tree
Uncertain data
Data collection process
Issue Date2009
PublisherI E E E, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178
Citation
Proceedings - International Conference On Data Engineering, 2009, p. 441-444 How to Cite?
AbstractTraditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information, which originates from measurement/quantisation errors, data staleness, multiple repeated measurements, etc. The value uncertainty is represented by multiple values forming a probability distribution function (pdf). We discover that the accuracy of a decision tree classifier can be much improved if the whole pdf, rather than a simple statistic, is taken into account. We extend classical decision tree building algorithms to handle data tuples with uncertain values. Since processing pdf's is computationally more costly, we propose a series of pruning techniques that can greatly improve the efficiency of the construction of decision trees. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/136223
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorTsang, Sen_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorYip, KYen_HK
dc.contributor.authorHo, WSen_HK
dc.contributor.authorLee, SDen_HK
dc.date.accessioned2011-07-27T02:05:00Z-
dc.date.available2011-07-27T02:05:00Z-
dc.date.issued2009en_HK
dc.identifier.citationProceedings - International Conference On Data Engineering, 2009, p. 441-444en_HK
dc.identifier.issn1084-4627en_HK
dc.identifier.urihttp://hdl.handle.net/10722/136223-
dc.description.abstractTraditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information, which originates from measurement/quantisation errors, data staleness, multiple repeated measurements, etc. The value uncertainty is represented by multiple values forming a probability distribution function (pdf). We discover that the accuracy of a decision tree classifier can be much improved if the whole pdf, rather than a simple statistic, is taken into account. We extend classical decision tree building algorithms to handle data tuples with uncertain values. Since processing pdf's is computationally more costly, we propose a series of pruning techniques that can greatly improve the efficiency of the construction of decision trees. © 2009 IEEE.en_HK
dc.languageengen_US
dc.publisherI E E E, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178en_HK
dc.relation.ispartofProceedings - International Conference on Data Engineeringen_HK
dc.rightsIEEE Transactions on Knowledge & Data Engineering. Copyright © IEEE.-
dc.rights©2011 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectClassification-
dc.subjectData mining-
dc.subjectDecision tree-
dc.subjectUncertain data-
dc.subjectData collection process-
dc.titleDecision trees for uncertain dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailKao, B: kao@cs.hku.hken_HK
dc.identifier.emailHo, WS: wsho@cs.hku.hken_HK
dc.identifier.authorityKao, B=rp00123en_HK
dc.identifier.authorityHo, WS=rp01730en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDE.2009.26en_HK
dc.identifier.scopuseid_2-s2.0-67649641455en_HK
dc.identifier.hkuros186897en_US
dc.identifier.hkuros152284-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-67649641455&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume23en_US
dc.identifier.issue1en_US
dc.identifier.spage441en_HK
dc.identifier.epage444en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridTsang, S=26666352300en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridYip, KY=7101909946en_HK
dc.identifier.scopusauthoridHo, WS=7402968940en_HK
dc.identifier.scopusauthoridLee, SD=7601400741en_HK
dc.identifier.citeulike7034206-

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