{"id":"https://openalex.org/W3093361771","doi":"https://doi.org/10.1109/icpr48806.2021.9412499","title":"Learning Parameter Distributions to Detect Concept Drift in Data Streams","display_name":"Learning Parameter Distributions to Detect Concept Drift in Data Streams","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3093361771","doi":"https://doi.org/10.1109/icpr48806.2021.9412499","mag":"3093361771"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2010.09388","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071493719","display_name":"Johannes Haug","orcid":"https://orcid.org/0000-0003-1286-3551"},"institutions":[{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Johannes Haug","raw_affiliation_strings":["University of Tuebingen, Tuebingen, Germany","University of T\u00fcbingen"],"affiliations":[{"raw_affiliation_string":"University of Tuebingen, Tuebingen, Germany","institution_ids":["https://openalex.org/I8087733"]},{"raw_affiliation_string":"University of T\u00fcbingen","institution_ids":["https://openalex.org/I8087733"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024434748","display_name":"Gjergji Kasneci","orcid":null},"institutions":[{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gjergji Kasneci","raw_affiliation_strings":["University of Tuebingen, Tuebingen, Germany","University of T\u00fcbingen"],"affiliations":[{"raw_affiliation_string":"University of Tuebingen, Tuebingen, Germany","institution_ids":["https://openalex.org/I8087733"]},{"raw_affiliation_string":"University of T\u00fcbingen","institution_ids":["https://openalex.org/I8087733"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071493719"],"corresponding_institution_ids":["https://openalex.org/I8087733"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60867637,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"9452","last_page":"9459"},"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/T11478","display_name":"Caching and Content Delivery","score":0.9858999848365784,"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"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/concept-drift","display_name":"Concept drift","score":0.9703693389892578},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7457032203674316},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.7263177633285522},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6782264113426208},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5972046256065369},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.5580666065216064},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5535839796066284},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5017781257629395},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.4373517632484436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3793911933898926},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37890517711639404},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13434576988220215}],"concepts":[{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.9703693389892578},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7457032203674316},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.7263177633285522},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6782264113426208},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5972046256065369},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.5580666065216064},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5535839796066284},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5017781257629395},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.4373517632484436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3793911933898926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37890517711639404},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13434576988220215},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2010.09388","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.09388","pdf_url":"https://arxiv.org/pdf/2010.09388","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3093361771","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2010.09388.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2010.09388","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2010.09388","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2010.09388","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.09388","pdf_url":"https://arxiv.org/pdf/2010.09388","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W27170557","https://openalex.org/W1505456515","https://openalex.org/W1567139212","https://openalex.org/W1585854823","https://openalex.org/W1642701485","https://openalex.org/W2010657328","https://openalex.org/W2052283750","https://openalex.org/W2069701377","https://openalex.org/W2099419573","https://openalex.org/W2134271388","https://openalex.org/W2143991132","https://openalex.org/W2151225423","https://openalex.org/W2165660393","https://openalex.org/W2244109919","https://openalex.org/W2517990807","https://openalex.org/W2533585993","https://openalex.org/W2605253252","https://openalex.org/W2623257591","https://openalex.org/W2747747656","https://openalex.org/W2751686959","https://openalex.org/W2794413384","https://openalex.org/W2962862931","https://openalex.org/W2963334956","https://openalex.org/W2963991843","https://openalex.org/W2972977938","https://openalex.org/W2975383353","https://openalex.org/W2979827629","https://openalex.org/W3036781224","https://openalex.org/W3080633080","https://openalex.org/W3105628676","https://openalex.org/W3120740533","https://openalex.org/W6635179022","https://openalex.org/W6684485356","https://openalex.org/W6737947904","https://openalex.org/W6752866118"],"related_works":["https://openalex.org/W3161172664","https://openalex.org/W3185230522","https://openalex.org/W2556653490","https://openalex.org/W2975383353","https://openalex.org/W2797144402","https://openalex.org/W2990081132","https://openalex.org/W2979827629","https://openalex.org/W3121685557","https://openalex.org/W2941914072","https://openalex.org/W3181992839","https://openalex.org/W2498500834","https://openalex.org/W3010959694","https://openalex.org/W960907006","https://openalex.org/W2082395561","https://openalex.org/W2897204721","https://openalex.org/W2747716660","https://openalex.org/W2255333239","https://openalex.org/W2890015240","https://openalex.org/W3135615975","https://openalex.org/W2801476745"],"abstract_inverted_index":{"Data":[0],"distributions":[1],"in":[2,95],"streaming":[3,56],"environments":[4],"are":[5,30],"usually":[6],"not":[7],"stationary.":[8],"In":[9,58],"order":[10],"to":[11,24,26,51,92,127],"maintain":[12],"a":[13,63,80,93,137],"high":[14],"predictive":[15,81],"quality":[16],"at":[17,131],"all":[18],"times,":[19],"online":[20],"learning":[21],"models":[22],"need":[23],"adapt":[25],"distributional":[27],"changes,":[28],"which":[29,135],"known":[31],"as":[32,46,83],"concept":[33,41,71,89,129,158],"drift.":[34],"The":[35,111],"timely":[36],"and":[37,148,162],"robust":[38],"identification":[39],"of":[40,55,69,79,98],"drift":[42,90,130,159],"can":[43],"be":[44],"difficult,":[45],"we":[47,61,86,104],"never":[48],"have":[49],"access":[50],"the":[52,67,77,96,132,154],"true":[53],"distribution":[54,97],"data.":[57],"this":[59,102],"work,":[60],"propose":[62],"novel":[64],"framework":[65,113,156],"for":[66],"detection":[68],"real":[70],"drift,":[72],"called":[73],"ERICS.":[74],"By":[75,117],"treating":[76],"parameters":[78],"model":[82],"random":[84],"variables,":[85],"show":[87],"that":[88,153],"corresponds":[91],"change":[94],"optimal":[99],"parameters.":[100],"To":[101],"end,":[103],"adopt":[105],"common":[106],"measures":[107],"from":[108],"information":[109],"theory.":[110],"proposed":[112,155],"is":[114,124,136],"completely":[115],"model-agnostic.":[116],"choosing":[118],"an":[119],"appropriate":[120],"base":[121],"model,":[122],"ERICS":[123],"also":[125],"capable":[126],"detect":[128],"input":[133],"level,":[134],"significant":[138],"advantage":[139],"over":[140],"existing":[141,166],"approaches.":[142],"An":[143],"evaluation":[144],"on":[145],"several":[146],"synthetic":[147],"real-world":[149],"data":[150],"sets":[151],"suggests":[152],"identifies":[157],"more":[160],"effectively":[161],"precisely":[163],"than":[164],"various":[165],"works.":[167]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
