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dc.contributor.authorDeb, Rupam
dc.contributor.authorLiew, Wee-Chung
dc.date.accessioned2019-06-14T04:24:17Z
dc.date.available2019-06-14T04:24:17Z
dc.date.issued2019
dc.identifier.issn0020-0255en_US
dc.identifier.doi10.1016/j.ins.2018.10.002en_US
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.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofpagefrom132en_US
dc.relation.ispartofpageto146en_US
dc.relation.ispartofjournalInformation Sciencesen_US
dc.relation.ispartofvolume476en_US
dc.subject.fieldofresearchMathematical Sciencesen_US
dc.subject.fieldofresearchInformation and Computing Sciencesen_US
dc.subject.fieldofresearchEngineeringen_US
dc.subject.fieldofresearchcode01en_US
dc.subject.fieldofresearchcode08en_US
dc.subject.fieldofresearchcode09en_US
dc.titleNoisy values detection and correction of traffic accident dataen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dc.type.codeC - Journal Articlesen_US
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.description.versionPost-printen_US
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.en_US
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