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dc.contributor.authorDeb, Rupamen_US
dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.editorX. Wang, W. Pedrycz, P. Chan, Q. Heen_US
dc.date.accessioned2018-06-15T01:30:30Z
dc.date.available2018-06-15T01:30:30Z
dc.date.issued2014en_US
dc.identifier.refurihttp://www.icmlc.comen_US
dc.identifier.doi10.1007/978-3-662-45652-1_28en_US
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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.publisherSpringeren_US
dc.publisher.placeGermanyen_US
dc.publisher.urihttp://www.icmlc.comen_US
dc.relation.ispartofstudentpublicationYen_US
dc.relation.ispartofconferencenameICMLC 2014en_US
dc.relation.ispartofconferencetitleProceedings of the 13th International Conference on Machine Learning and Cyberneticsen_US
dc.relation.ispartofdatefrom2014-07-13en_US
dc.relation.ispartofdateto2014-07-16en_US
dc.relation.ispartoflocationLanzhou, Chinaen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080109en_US
dc.titleMissing Value Imputation for the Analysis of Incomplete Traffic Accident Dataen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
dc.description.versionPost-printen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
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.comen_US
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