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dc.contributor.authorDeb, Rupam
dc.contributor.authorLiew, Alan Wee-Chung
dc.date.accessioned2019-06-14T04:24:17Z
dc.date.available2019-06-14T04:24:17Z
dc.date.issued2019
dc.identifier.issn0020-0255
dc.identifier.doi10.1016/j.ins.2018.10.002
dc.identifier.urihttp://hdl.handle.net/10072/382927
dc.description.abstractDeath, injury, and disability from road traffic crashes continue to be a major global public health problem. Therefore, methods to reduce accident severity are of significant interest to traffic agencies and the public at large. Noisy data in the traffic accident dataset obscure the discovery of important factors and mislead conclusions. Identifying and correcting noisy values is an important goal of data cleansing and preprocessing. This paper proposes a new algorithm called NoiseCleaner to identify and correct noisy categorical attributes values in large traffic accident datasets. We evaluate our algorithm using four publicly available traffic accident datasets from Australia and United States, namely, two road crash datasets from the Queensland Government data depository (data.qld.gov.au) and two datasets from the New York's open data portal (data.ny.gov). We compare our technique with several existing state-of-the-art methods and show that our algorithm performs significantly better than the existing algorithms.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherELSEVIER SCIENCE INC
dc.relation.ispartofpagefrom132
dc.relation.ispartofpageto146
dc.relation.ispartofjournalInformation Sciences
dc.relation.ispartofvolume476
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.titleNoisy values detection and correction of traffic accident data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionPost-print
gro.rights.copyright© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


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