{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:07:39Z","timestamp":1772118459233,"version":"3.50.1"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"name":"Nature Science Foundation of China","award":["61662024"],"award-info":[{"award-number":["61662024"]}]},{"name":"Nature Science Foundation of China","award":["61602220"],"award-info":[{"award-number":["61602220"]}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20171BAB212013"],"award-info":[{"award-number":["20171BAB212013"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20161BBI90004"],"award-info":[{"award-number":["20161BBI90004"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20161BAB212057"],"award-info":[{"award-number":["20161BAB212057"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of the Hubei Province Key Laboratory","award":["2016KLA03"],"award-info":[{"award-number":["2016KLA03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2017]]},"DOI":"10.1109\/access.2017.2766203","type":"journal-article","created":{"date-parts":[[2017,10,25]],"date-time":"2017-10-25T15:35:51Z","timestamp":1508945751000},"page":"24417-24425","source":"Crossref","is-referenced-by-count":100,"title":["An Ensemble Deep Learning Method for Vehicle Type Classification on Visual Traffic Surveillance Sensors"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5463-9991","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miaohui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanzheng","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/ICDAR.1995.598994","article-title":"Random decision forests","volume":"1","author":"ho","year":"1995","journal-title":"Proc 3rd Int Conf Document Anal Recognition"},{"key":"ref32","article-title":"Bagging predictors","author":"breiman","year":"1994"},{"key":"ref31","first-page":"411","author":"zhou","year":"2015","journal-title":"Ensemble Learning"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ISM.2015.126"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1162\/coli.07-034-R2"},{"key":"ref36","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref35","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref34","first-page":"479","article-title":"On the margin explanation of boosting algorithms","author":"wang","year":"2008","journal-title":"Proc COLT"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"chawla","year":"2002","journal-title":"J Artif Intell Res"},{"key":"ref11","first-page":"1","article-title":"C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling","volume":"11","author":"drummond","year":"2003","journal-title":"Proc Workshop Learning from Imbalanced Datasets II"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/11538059_91"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CIDM.2011.5949434"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312220"},{"key":"ref16","first-page":"983","article-title":"A comparative study of cost-sensitive boosting algorithms","author":"ting","year":"2000","journal-title":"Proc 17th Int Conf Mach Learn"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IRI.2014.7051939"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007735"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICM.2011.34"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17534-3_19"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2003.1250950"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.10.005"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.17"},{"key":"ref29","author":"khan","year":"2015","journal-title":"Cost sensitive learning of deep feature representations from imbalanced data"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.9.2122"},{"key":"ref8","article-title":"Using random forest to learn imbalanced data","volume":"110","author":"chen","year":"2004"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.580"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-015-0450-0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2008.2002909"},{"key":"ref1","first-page":"3013","article-title":"Towards 3D object detection with bimodal deep Boltzmann machines over RGBD imagery","author":"liu","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","article-title":"Exploratory undersampling for class-imbalance learning","volume":"39","author":"liu","year":"2009","journal-title":"IEEE Trans Syst Man B (Cybern )"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref21","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.223"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref25","author":"simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/7859429\/08082506.pdf?arnumber=8082506","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:30:06Z","timestamp":1641987006000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8082506\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2017.2766203","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}