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dc.contributor.authorDeb, R
dc.contributor.authorLiew, AWC
dc.contributor.editorX. Wang, W. Pedrycz, P. Chan, Q. He
dc.date.accessioned2018-06-15T01:30:30Z
dc.date.available2018-06-15T01:30:30Z
dc.date.issued2014
dc.identifier.isbn9783662456514
dc.identifier.issn1865-0929
dc.identifier.refurihttp://www.icmlc.com
dc.identifier.doi10.1007/978-3-662-45652-1_28
dc.identifier.urihttp://hdl.handle.net/10072/66724
dc.description.abstractRoad traffic accidents are a major public health concern, resulting in an estimated 1.3 million deaths and 52 million injuries worldwide each year. All the developed and developing countries suffer from the consequences of increase in both human and vehicle population. Therefore, methods to reduce accident severity are of great interest to traffic agencies and the public at large. To analysis the traffic accident factors effectively we need a complete traffic accident historical database without missing data. Road accident fatality rate depends on many factors and it is a very challenging task to investigate the dependencies between the attributes because of the many environmental and road accident factors. Any missing data in the database could obscure the discovery of important factors and lead to invalid conclusions. In order to make the traffic accident datasets useful for analysis, it should be preprocessed properly. In this paper, we present a novel method based on decision tree and imputed value sampling based on correlation measure for the imputation of missing values to improve the quality of the traffic accident data. We applied our algorithm to the publicly available large traffic accident database of United States (explore.data.gov), which is the largest open federal database in United States. We compare our algorithm with three existing imputation methods using three evaluation criteria, i.e. mean absolute error, coefficient of determination and root mean square error. Our results indicate that the proposed method performs significantly better than the three existing algorithms.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherSpringer
dc.publisher.placeGermany
dc.publisher.urihttp://www.icmlc.com
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameICMLC 2014
dc.relation.ispartofconferencetitleCommunications in Computer and Information Science
dc.relation.ispartofdatefrom2014-07-13
dc.relation.ispartofdateto2014-07-16
dc.relation.ispartoflocationLanzhou, China
dc.relation.ispartofpagefrom275
dc.relation.ispartofpageto286
dc.relation.ispartofvolume481
dc.rights.retentionY
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleMissing Value Imputation for the Analysis of Incomplete Traffic Accident Data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionPost-print
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2014 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorDeb, Rupam


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    Contains papers delivered by Griffith authors at national and international conferences.

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