{"id":"https://openalex.org/W2895867838","doi":"https://doi.org/10.1109/ijcnn.2018.8489364","title":"A Drift Detection Method Based on Active Learning","display_name":"A Drift Detection Method Based on Active Learning","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2895867838","doi":"https://doi.org/10.1109/ijcnn.2018.8489364","mag":"2895867838"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5073222746","display_name":"Albert Fran\u00e7a Josu\u00e1 Costa","orcid":"https://orcid.org/0000-0001-7445-2331"},"institutions":[{"id":"https://openalex.org/I4210148508","display_name":"Instituto Federal do Amazonas","ror":"https://ror.org/045nsn047","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210148508"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Albert Franca Josua Costa","raw_affiliation_strings":["Federal Institute of Amazonas, Humait\u00e1, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal Institute of Amazonas, Humait\u00e1, Brazil","institution_ids":["https://openalex.org/I4210148508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074270959","display_name":"Regis Antonio Saraiva Albuquerque","orcid":"https://orcid.org/0009-0006-3473-4362"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Regis Antonio Saraiva Albuquerque","raw_affiliation_strings":["Institute Computing - ICOMP, Federal University of Amazonas, Manaus, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute Computing - ICOMP, Federal University of Amazonas, Manaus, Brazil","institution_ids":["https://openalex.org/I62885914"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029548136","display_name":"Eulanda M. dos Santos","orcid":"https://orcid.org/0000-0002-5671-581X"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Eulanda Miranda dos Santos","raw_affiliation_strings":["Institute Computing - ICOMP, Federal University of Amazonas, Manaus, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute Computing - ICOMP, Federal University of Amazonas, Manaus, Brazil","institution_ids":["https://openalex.org/I62885914"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1318,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.8401154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":1.0,"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/T12761","display_name":"Data Stream Mining Techniques","score":1.0,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9843999743461609,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9789999723434448,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7460237145423889},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.735491931438446},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.727029025554657},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6322280168533325},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5577234625816345},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5392400622367859},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5349660515785217},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4496493935585022},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.43754279613494873},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.41587361693382263},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4151468276977539},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3608517050743103},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.11063149571418762}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7460237145423889},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.735491931438446},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.727029025554657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6322280168533325},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5577234625816345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5392400622367859},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5349660515785217},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4496493935585022},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.43754279613494873},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.41587361693382263},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4151468276977539},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3608517050743103},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.11063149571418762},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W27170557","https://openalex.org/W1492483411","https://openalex.org/W1873218251","https://openalex.org/W1982039810","https://openalex.org/W1991076541","https://openalex.org/W2060206013","https://openalex.org/W2139327121","https://openalex.org/W2140164381","https://openalex.org/W2145940431","https://openalex.org/W2249134605","https://openalex.org/W2505204805","https://openalex.org/W2562656267","https://openalex.org/W2605253252","https://openalex.org/W2796293253","https://openalex.org/W4285719527","https://openalex.org/W6629275181","https://openalex.org/W6680844135"],"related_works":["https://openalex.org/W3162567751","https://openalex.org/W4312414840","https://openalex.org/W4294974824","https://openalex.org/W2538661024","https://openalex.org/W2107003417","https://openalex.org/W1988412055","https://openalex.org/W2914973938","https://openalex.org/W2542037019","https://openalex.org/W3001698026","https://openalex.org/W2151561819"],"abstract_inverted_index":{"Several":[0],"real-world":[1],"prediction":[2],"problems":[3],"are":[4,57],"subject":[5],"to":[6,11,22,32,119,124,161,183],"changes":[7],"over":[8],"time":[9],"due":[10],"their":[12],"dynamic":[13],"nature.":[14],"These":[15],"changes,":[16],"named":[17],"concept":[18,95],"drift,":[19],"usually":[20],"lead":[21],"immediate":[23],"and":[24,106,143,157,169,223,248],"disastrous":[25],"loss":[26],"in":[27,45,76,154,191],"classifier's":[28],"performance.":[29],"In":[30,122,205],"order":[31,123],"cope":[33],"with":[34],"such":[35],"a":[36,240],"serious":[37],"problem,":[38],"drift":[39,96,167],"detection":[40,203,224,249],"methods":[41,50],"have":[42,133],"been":[43],"proposed":[44,130,237],"the":[46,85,115,126,129,184,192,195,208,216,228,236],"literature.":[47],"However,":[48],"current":[49],"cannot":[51],"be":[52],"widely":[53],"used":[54],"since":[55],"they":[56],"based":[58,102],"either":[59],"on":[60,64,72,103,137,148,158],"performance":[61,127],"monitoring":[62],"or":[63,68,201],"fully":[65],"labeled":[66,233,246],"data,":[67],"even":[69,226],"both.":[70],"Focusing":[71],"overcoming":[73],"these":[74],"drawbacks,":[75],"this":[77],"work":[78],"we":[79,132],"propose":[80],"using":[81],"density":[82,99],"variation":[83,100],"of":[84,128,194,207,245],"most":[86],"significant":[87],"instances":[88],"as":[89,251,253],"an":[90],"explicit":[91],"unsupervised":[92],"trigger":[93],"for":[94],"detection.":[97],"Here,":[98],"is":[101,108],"Active":[104,171,209],"Learning,":[105],"it":[107,160],"calculated":[109],"from":[110],"virtual":[111],"margins":[112],"projected":[113],"onto":[114],"input":[116],"space":[117],"according":[118],"classifier":[120],"confidence.":[121],"investigate":[125],"method,":[131,180],"carried":[134],"out":[135],"experiments":[136],"six":[138],"databases,":[139,197],"precisely":[140],"four":[141],"synthetic":[142],"two":[144,165],"real":[145],"databases":[146],"focusing":[147],"setting":[149],"up":[150],"all":[151],"parameters":[152],"involved":[153],"our":[155,179,213],"method":[156,214,238],"comparing":[159],"three":[162],"baselines,":[163,186],"including":[164],"supervised":[166,185],"detectors":[168],"one":[170],"Learning-based":[172,210],"strategy.":[173],"The":[174],"obtained":[175],"results":[176],"show":[177],"that":[178],"when":[181],"compared":[182],"reached":[187],"better":[188,241],"recognition":[189,222,254],"rates":[190],"majority":[193],"investigated":[196],"while":[198],"keeping":[199],"similar":[200],"higher":[202],"rates.":[204],"terms":[206],"strategies":[211],"comparison,":[212],"outperformed":[215],"baseline":[217,229],"taking":[218],"into":[219],"account":[220],"both":[221],"rates,":[225],"though":[227],"employed":[230],"much":[231],"less":[232],"samples.":[234],"Therefore,":[235],"established":[239],"trade-off":[242],"between":[243],"amount":[244],"samples":[247],"capability,":[250],"well":[252],"rate.":[255]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}