{"id":"https://openalex.org/W2963536745","doi":"https://doi.org/10.2352/issn.2470-1173.2017.9.iriacv-272","title":"Outlier Detection in Large-Scale Traffic Data by Naïve Bayes Method and Gaussian Mixture Model Method","display_name":"Outlier Detection in Large-Scale Traffic Data by Naïve Bayes Method and Gaussian Mixture Model Method","publication_year":2017,"publication_date":"2017-01-29","ids":{"openalex":"https://openalex.org/W2963536745","doi":"https://doi.org/10.2352/issn.2470-1173.2017.9.iriacv-272","mag":"2963536745"},"language":"en","primary_location":{"id":"doi:10.2352/issn.2470-1173.2017.9.iriacv-272","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2017.9.iriacv-272","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","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/A5054929368","display_name":"Philip Lam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Philip Lam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100356430","display_name":"Lili Wang","orcid":"https://orcid.org/0000-0003-3228-4290"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lili Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021455647","display_name":"Henry Y. T. Ngan","orcid":"https://orcid.org/0000-0003-0382-952X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Henry Y.T. Ngan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111714815","display_name":"N.H.C. Yung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nelson H.C. Yung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112204804","display_name":"Anthony G.O. Yeh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anthony G.O. Yeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7944,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.89286193,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"29","issue":"9","first_page":"73","last_page":"78"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9986000061035156,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9986000061035156,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9553999900817871,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/outlier","display_name":"Outlier","score":0.7111784815788269},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.701508641242981},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5988742709159851},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5827823281288147},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5139860510826111},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4651449918746948},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46302857995033264},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4407355785369873},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42099565267562866},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4196890592575073},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3715910315513611},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2880820035934448},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.15884676575660706},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09202876687049866}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7111784815788269},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.701508641242981},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5988742709159851},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5827823281288147},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5139860510826111},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4651449918746948},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46302857995033264},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4407355785369873},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42099565267562866},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4196890592575073},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3715910315513611},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2880820035934448},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.15884676575660706},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09202876687049866},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2352/issn.2470-1173.2017.9.iriacv-272","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2017.9.iriacv-272","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3133833924","https://openalex.org/W81524370","https://openalex.org/W2380927352","https://openalex.org/W3178621026","https://openalex.org/W2137598809","https://openalex.org/W2085553065","https://openalex.org/W1595351371","https://openalex.org/W3116762327","https://openalex.org/W2230433129","https://openalex.org/W2076520961"],"abstract_inverted_index":{"It":[0],"is":[1,14,161],"meaningful":[2],"to":[3,23,45,78,91,149,204],"detect":[4,47],"outliers":[5],"in":[6,57,65,72,124,142],"traffic":[7,10,55,58,81,129,143,159],"data":[8,59,69,110,144,160],"for":[9,18,103,128,173],"management.":[11],"However,":[12],"this":[13,27],"a":[15,62,73,92],"massive":[16],"task":[17],"people":[19],"from":[20],"largescale":[21],"database":[22],"distinguish":[24],"outliers.":[25],"In":[26,152],"paper,":[28],"we":[29],"present":[30],"two":[31],"methods:":[32],"Kernel":[33],"Smoothing":[34],"Naïve":[35],"Bayes":[36],"(NB)":[37],"method":[38,44,135,156,183,201],"and":[39,76,118,145,169,184,199,206],"Gaussian":[40,167],"Mixture":[41],"Model":[42],"(GMM)":[43],"automatically":[46],"any":[48],"hardware":[49],"errors":[50],"as":[51,53],"well":[52],"abnormal":[54],"events":[56],"collected":[60],"at":[61],"four-arm":[63],"junction":[64],"Hong":[66],"Kong.":[67],"Traffic":[68],"was":[70],"recorded":[71],"video":[74],"format,":[75],"converted":[77],"spatial-temporal":[79],"(ST)":[80],"signals":[82,87],"by":[83,98,163],"statistics.":[84],"The":[85,131],"ST":[86],"are":[88,111,121],"then":[89],"projected":[90],"two-dimensional":[93],"(2D)":[94],"(x,":[95],"y)-coordinate":[96],"plane":[97],"Principal":[99],"Component":[100],"Analysis":[101],"(PCA)":[102],"dimension":[104],"reduction.":[105],"We":[106],"assume":[107],"that":[108,192],"inlier":[109],"normal":[112],"distributed.":[113],"As":[114],"such,":[115],"the":[116,137,154,158,164,179,193],"NB":[117,134,194],"GMM":[119,155,200],"methods":[120],"successfully":[122],"applied":[123],"OD":[125],"(Outlier":[126],"Detection)":[127],"data.":[130],"kernel":[132,140,198],"smooth":[133],"assumes":[136],"existence":[138],"of":[139,166,181],"distributions":[141,168],"uses":[146],"Bayes'":[147],"Theorem":[148],"perform":[150],"OD.":[151,174],"contrast,":[153],"believes":[157],"formed":[162],"mixture":[165],"exploits":[170],"confidence":[171],"region":[172],"This":[175],"paper":[176],"would":[177],"address":[178],"modeling":[180],"each":[182],"evaluate":[185],"their":[186],"respective":[187],"performances.":[188],"Experimental":[189],"results":[190],"show":[191],"algorithm":[195],"with":[196],"Triangle":[197],"achieve":[202],"up":[203],"93.78%":[205],"94.50%":[207],"accuracies,":[208],"respectively.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}