{"id":"https://openalex.org/W4226328956","doi":"https://doi.org/10.1109/bigdata52589.2021.9671279","title":"Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels","display_name":"Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4226328956","doi":"https://doi.org/10.1109/bigdata52589.2021.9671279"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671279","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5101798266","display_name":"Yiming Xu","orcid":"https://orcid.org/0000-0003-3011-700X"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yiming Xu","raw_affiliation_strings":["Northwestern University, Evanston, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013049879","display_name":"Diego Klabjan","orcid":"https://orcid.org/0000-0003-4213-9281"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Diego Klabjan","raw_affiliation_strings":["Northwestern University, Evanston, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, USA","institution_ids":["https://openalex.org/I111979921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101798266"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":0.5026,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.684427,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1504","last_page":"1513"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9769999980926514,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9603000283241272,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7263029217720032},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6646742224693298},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6644463539123535},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.5820438265800476},{"id":"https://openalex.org/keywords/lag","display_name":"Lag","score":0.5714027285575867},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5558388233184814},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5540769696235657},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5321207642555237},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5043410062789917},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4760972261428833},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.47097349166870117},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.46450918912887573},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40557265281677246},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.20166993141174316},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09111931920051575}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7263029217720032},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6646742224693298},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6644463539123535},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5820438265800476},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.5714027285575867},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5558388233184814},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5540769696235657},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5321207642555237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5043410062789917},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4760972261428833},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.47097349166870117},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.46450918912887573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40557265281677246},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.20166993141174316},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09111931920051575},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671279","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671279","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W27170557","https://openalex.org/W1585854823","https://openalex.org/W1990079212","https://openalex.org/W2051903196","https://openalex.org/W2052283750","https://openalex.org/W2069701377","https://openalex.org/W2075721911","https://openalex.org/W2076170369","https://openalex.org/W2088220893","https://openalex.org/W2112796928","https://openalex.org/W2143991132","https://openalex.org/W2155894447","https://openalex.org/W2157664934","https://openalex.org/W2310485631","https://openalex.org/W2605253252","https://openalex.org/W2791425844","https://openalex.org/W3101213140","https://openalex.org/W3110899636","https://openalex.org/W6635179022","https://openalex.org/W6683107984","https://openalex.org/W6785494765","https://openalex.org/W6787005880","https://openalex.org/W6907470458"],"related_works":["https://openalex.org/W2985746494","https://openalex.org/W2990081132","https://openalex.org/W4206042385","https://openalex.org/W4296984035","https://openalex.org/W2511384863","https://openalex.org/W2080773131","https://openalex.org/W2096089271","https://openalex.org/W2923628599","https://openalex.org/W2014100433","https://openalex.org/W2051519658"],"abstract_inverted_index":{"In":[0],"model":[1,6,18,33,47,91],"serving,":[2],"having":[3],"one":[4],"fixed":[5],"during":[7],"the":[8,32,46,90,97,117,138,154,175],"entire":[9],"often":[10],"life-long":[11],"inference":[12],"process":[13],"is":[14,39],"usually":[15],"detrimental":[16],"to":[17,41,82,86,88,105,148,150],"performance,":[19],"as":[20,63],"data":[21,85,147,162,167],"distribution":[22],"evolves":[23],"over":[24],"time,":[25],"resulting":[26],"in":[27,48,135],"lack":[28],"of":[29,31,110,112,123,127,166],"reliability":[30],"trained":[34],"on":[35,153,158],"historical":[36],"data.":[37],"It":[38],"important":[40],"detect":[42],"changes":[43,168],"and":[44,79,114,160],"retrain":[45,89,151],"time.":[49,136],"The":[50],"existing":[51],"methods":[52,177],"generally":[53],"have":[54],"three":[55],"weaknesses:":[56],"1)":[57],"using":[58],"only":[59],"classification":[60],"error":[61],"rate":[62],"signal,":[64],"2)":[65],"assuming":[66],"ground":[67],"truth":[68],"labels":[69,126],"are":[70,77,130],"immediately":[71],"available":[72],"after":[73,132],"features":[74,129],"from":[75],"samples":[76],"received":[78,131],"3)":[80],"unable":[81],"decide":[83],"what":[84,146],"use":[87,149],"when":[92],"change":[93],"occurs.":[94],"We":[95],"address":[96,116],"first":[98],"problem":[99,119],"by":[100,120,178],"utilizing":[101],"six":[102],"different":[103,164],"signals":[104],"capture":[106],"a":[107,133,179],"wide":[108],"range":[109],"characteristics":[111],"data,":[113],"we":[115],"second":[118],"allowing":[121],"lag":[122,134],"labels,":[124],"where":[125],"corresponding":[128],"For":[137],"third":[139],"problem,":[140],"our":[141,171],"proposed":[142],"method":[143,172],"automatically":[144],"decides":[145],"based":[152],"signals.":[155],"Extensive":[156],"experiments":[157],"structured":[159],"unstructured":[161],"for":[163],"type":[165],"establish":[169],"that":[170],"consistently":[173],"outperforms":[174],"state-of-the-art":[176],"large":[180],"margin.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-01-29T23:13:10.619473","created_date":"2025-10-10T00:00:00"}