{"id":"https://openalex.org/W2093579976","doi":"https://doi.org/10.1108/ijicc-06-2013-0030","title":"Vehicle identification by improved stacking via kernel principal component regression","display_name":"Vehicle identification by improved stacking via kernel principal component regression","publication_year":2014,"publication_date":"2014-11-04","ids":{"openalex":"https://openalex.org/W2093579976","doi":"https://doi.org/10.1108/ijicc-06-2013-0030","mag":"2093579976"},"language":"en","primary_location":{"id":"doi:10.1108/ijicc-06-2013-0030","is_oa":false,"landing_page_url":"https://doi.org/10.1108/ijicc-06-2013-0030","pdf_url":null,"source":{"id":"https://openalex.org/S124503262","display_name":"International Journal of Intelligent Computing and Cybernetics","issn_l":"1756-378X","issn":["1756-378X","1756-3798"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Computing and Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086628680","display_name":"Bailing Zhang","orcid":"https://orcid.org/0000-0002-5472-2100"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bailing Zhang","raw_affiliation_strings":["Department of Computer Science and Software Engineering, Xi\u2019an Jiaotong-Liverpool University, Suzhou, China","Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Xi\u2019an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]},{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740620","display_name":"Hao Pan","orcid":"https://orcid.org/0000-0002-2531-0107"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Pan","raw_affiliation_strings":["Lucas Varity Langzhong Brake Co. Ltd, Langfang Development Zone, Hebei, China","(Lucas Varity Langzhong Brake Co. Ltd, Langfang Development Zone, Hebei, China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lucas Varity Langzhong Brake Co. Ltd, Langfang Development Zone, Hebei, China","institution_ids":[]},{"raw_affiliation_string":"(Lucas Varity Langzhong Brake Co. Ltd, Langfang Development Zone, Hebei, China)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.11156352,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"7","issue":"4","first_page":"415","last_page":"435"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7710568904876709},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6815113425254822},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6343785524368286},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5836958885192871},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5510101318359375},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.525428056716919},{"id":"https://openalex.org/keywords/decision-boundary","display_name":"Decision boundary","score":0.4796157777309418},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4517870545387268},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.45147058367729187},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.4362751245498657},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.43113037943840027},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.41948607563972473},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3539623022079468}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7710568904876709},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6815113425254822},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6343785524368286},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5836958885192871},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5510101318359375},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.525428056716919},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.4796157777309418},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4517870545387268},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.45147058367729187},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4362751245498657},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.43113037943840027},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.41948607563972473},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3539623022079468}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/ijicc-06-2013-0030","is_oa":false,"landing_page_url":"https://doi.org/10.1108/ijicc-06-2013-0030","pdf_url":null,"source":{"id":"https://openalex.org/S124503262","display_name":"International Journal of Intelligent Computing and Cybernetics","issn_l":"1756-378X","issn":["1756-378X","1756-3798"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Computing and Cybernetics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5899999737739563,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W1506281249","https://openalex.org/W1510073064","https://openalex.org/W1549436606","https://openalex.org/W1554663460","https://openalex.org/W1579271636","https://openalex.org/W1645816215","https://openalex.org/W1989746184","https://openalex.org/W2016648380","https://openalex.org/W2035710656","https://openalex.org/W2040895929","https://openalex.org/W2042218323","https://openalex.org/W2064236088","https://openalex.org/W2088032561","https://openalex.org/W2099695159","https://openalex.org/W2105847589","https://openalex.org/W2119821739","https://openalex.org/W2120820227","https://openalex.org/W2124868070","https://openalex.org/W2132461991","https://openalex.org/W2134380836","https://openalex.org/W2138273245","https://openalex.org/W2139269942","https://openalex.org/W2146769544","https://openalex.org/W2160767978","https://openalex.org/W2161969291","https://openalex.org/W2171786422","https://openalex.org/W2172000360","https://openalex.org/W2309693750","https://openalex.org/W2337231200","https://openalex.org/W2487087946","https://openalex.org/W2488678869","https://openalex.org/W3097096317","https://openalex.org/W3100344990","https://openalex.org/W4205686602","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W1981866886","https://openalex.org/W2052615004","https://openalex.org/W2099182244","https://openalex.org/W2046975922","https://openalex.org/W1580150424","https://openalex.org/W2380748039","https://openalex.org/W2325482571","https://openalex.org/W4361733484","https://openalex.org/W4256395896","https://openalex.org/W4241032203"],"abstract_inverted_index":{"Purpose":[0],"\u2013":[1,113,162,222],"Many":[2],"applications":[3],"in":[4],"intelligent":[5],"transportation":[6],"demand":[7],"accurate":[8],"categorization":[9],"of":[10,14,39,59,87,132,173,184,200,234],"vehicles.":[11],"The":[12,26,50,127,188],"purpose":[13],"this":[15,60],"paper":[16],"is":[17,31,56,62,117],"to":[18,80,198],"propose":[19],"a":[20,88,133,232],"working":[21],"image-based":[22],"vehicle":[23,29,53,110],"classification":[24],"system.":[25],"first":[27],"component":[28,52,124],"detection":[30],"implemented":[32],"by":[33,64],"applying":[34],"Dalal":[35],"and":[36,43,159,179,206,218,238],"Triggs's":[37],"histograms":[38],"oriented":[40],"gradients":[41],"features":[42],"linear":[44,100,142],"support":[45],"vector":[46],"machine":[47],"(SVM)":[48],"classifier.":[49],"second":[51],"classification,":[54],"which":[55],"the":[57,85,95,109,182,185,201,224,228],"emphasis":[58],"paper,":[61],"accomplished":[63],"an":[65,70],"improved":[66,189],"stacked":[67,75,104,128,190],"generalization.":[68],"As":[69],"effective":[71],"ensemble":[72,136],"learning":[73,210],"strategy,":[74],"generalization":[76,105,129,191],"has":[77],"been":[78],"proposed":[79,119,186],"combine":[81],"multiple":[82,156],"models":[83],"using":[84,165],"concept":[86],"meta-learner.":[89],"However,":[90],"it":[91],"was":[92],"found":[93],"that":[94],"well-known":[96],"meta-learning":[97],"scheme":[98,130],"multi-response":[99],"regression":[101,125,217],"(MLR)":[102],"for":[103,247],"performs":[106],"poorly":[107],"on":[108,121],"classification.":[111],"Design/methodology/approach":[112],"A":[114],"new":[115],"meta-learner":[116],"then":[118],"based":[120],"kernel":[122],"principal":[123],"(KPCR).":[126],"consists":[131],"heterogeneous":[134],"classifier":[135,204],"with":[137],"seven":[138,225],"base":[139,203,226],"classifiers,":[140,227],"i.e.":[141],"discriminant":[143],"classifier,":[144,152],"fuzzy":[145],"k":[146],"-nearest":[147],"neighbor,":[148],"logistic":[149,216],"regression,":[150],"Parzen":[151],"Gaussian":[153],"mixture":[154],"model,":[155],"layer":[157],"perceptron":[158],"SVM.":[160],"Findings":[161],"Experimental":[163],"results":[164,195],"more":[166],"than":[167],"2,500":[168],"images":[169],"from":[170],"four":[171,207],"types":[172],"vehicles":[174],"(bus,":[175],"light":[176],"truck,":[177],"car":[178],"van)":[180],"demonstrated":[181],"effectiveness":[183],"approach.":[187],"produced":[192],"consistently":[193],"better":[194],"when":[196],"compared":[197],"any":[199],"single":[202],"used":[205],"other":[208],"beta":[209],"algorithms,":[211],"including":[212],"MLR,":[213],"majority":[214],"voting,":[215],"decision":[219],"template.":[220],"Originality/value":[221],"With":[223],"KPCR-based":[229],"stacking":[230],"offers":[231],"performance":[233],"96":[235],"percent":[236,240],"accuracy":[237],"95":[239],"\u03ba":[241],"coefficient,":[242],"thus":[243],"exhibiting":[244],"promising":[245],"potentials":[246],"real-world":[248],"applications.":[249]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
