{"id":"https://openalex.org/W3129132077","doi":"https://doi.org/10.1007/s10618-021-00739-7","title":"Detecting virtual concept drift of regressors without ground truth values","display_name":"Detecting virtual concept drift of regressors without ground truth values","publication_year":2021,"publication_date":"2021-02-04","ids":{"openalex":"https://openalex.org/W3129132077","doi":"https://doi.org/10.1007/s10618-021-00739-7","mag":"3129132077"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-021-00739-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00739-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00739-7.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00739-7.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039712529","display_name":"Emilia Oikarinen","orcid":"https://orcid.org/0000-0002-9623-6282"},"institutions":[{"id":"https://openalex.org/I133731052","display_name":"University of Helsinki","ror":"https://ror.org/040af2s02","country_code":"FI","type":"education","lineage":["https://openalex.org/I133731052"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Emilia Oikarinen","raw_affiliation_strings":["Department of Computer Science, University of Helsinki, Helsinki, Finland"],"raw_orcid":"https://orcid.org/0000-0002-9623-6282","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Helsinki, Helsinki, Finland","institution_ids":["https://openalex.org/I133731052"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042349527","display_name":"Henri Tiittanen","orcid":null},"institutions":[{"id":"https://openalex.org/I133731052","display_name":"University of Helsinki","ror":"https://ror.org/040af2s02","country_code":"FI","type":"education","lineage":["https://openalex.org/I133731052"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Henri Tiittanen","raw_affiliation_strings":["Department of Computer Science, University of Helsinki, Helsinki, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Helsinki, Helsinki, Finland","institution_ids":["https://openalex.org/I133731052"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044047404","display_name":"Andreas Henelius","orcid":"https://orcid.org/0000-0002-4040-6967"},"institutions":[{"id":"https://openalex.org/I133731052","display_name":"University of Helsinki","ror":"https://ror.org/040af2s02","country_code":"FI","type":"education","lineage":["https://openalex.org/I133731052"]},{"id":"https://openalex.org/I4210089009","display_name":"OP Financial Group (Finland)","ror":"https://ror.org/006t5z227","country_code":"FI","type":"company","lineage":["https://openalex.org/I4210089009"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Andreas Henelius","raw_affiliation_strings":["Department of Computer Science, University of Helsinki, Helsinki, Finland","OP Financial Group, Helsinki, Finland"],"raw_orcid":"https://orcid.org/0000-0002-4040-6967","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Helsinki, Helsinki, Finland","institution_ids":["https://openalex.org/I133731052"]},{"raw_affiliation_string":"OP Financial Group, Helsinki, Finland","institution_ids":["https://openalex.org/I4210089009"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067547881","display_name":"Kai Puolam\u00e4ki","orcid":"https://orcid.org/0000-0003-1819-1047"},"institutions":[{"id":"https://openalex.org/I133731052","display_name":"University of Helsinki","ror":"https://ror.org/040af2s02","country_code":"FI","type":"education","lineage":["https://openalex.org/I133731052"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Kai Puolam\u00e4ki","raw_affiliation_strings":["Department of Computer Science, University of Helsinki, Helsinki, Finland","Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki, Finland"],"raw_orcid":"https://orcid.org/0000-0003-1819-1047","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Helsinki, Helsinki, Finland","institution_ids":["https://openalex.org/I133731052"]},{"raw_affiliation_string":"Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Helsinki, Finland","institution_ids":["https://openalex.org/I133731052"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039712529"],"corresponding_institution_ids":["https://openalex.org/I133731052"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":2.9957,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.92302442,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"35","issue":"3","first_page":"726","last_page":"747"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.979200005531311,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9703999757766724,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.92357337474823},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.77617347240448},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.6181584596633911},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6163074970245361},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5904141664505005},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5796827673912048},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.5779551267623901},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5723761916160583},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5121800899505615},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4959791600704193},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4879016578197479},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.4334411025047302},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4263293147087097},{"id":"https://openalex.org/keywords/generalization-error","display_name":"Generalization error","score":0.420383483171463},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2973164916038513},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2238365113735199},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12106552720069885},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.078396737575531}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.92357337474823},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.77617347240448},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.6181584596633911},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6163074970245361},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5904141664505005},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5796827673912048},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5779551267623901},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5723761916160583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5121800899505615},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4959791600704193},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4879016578197479},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.4334411025047302},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4263293147087097},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.420383483171463},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2973164916038513},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2238365113735199},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12106552720069885},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.078396737575531},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"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":2,"locations":[{"id":"doi:10.1007/s10618-021-00739-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00739-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00739-7.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},{"id":"pmh:oai:helda.helsinki.fi:10138/329715","is_oa":true,"landing_page_url":"http://hdl.handle.net/10138/329715","pdf_url":null,"source":{"id":"https://openalex.org/S4210213322","display_name":"Ty\u00f6v\u00e4entutkimus Vuosikirja","issn_l":"0784-1272","issn":["0784-1272","1459-7780"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1007/s10618-021-00739-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00739-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00739-7.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1247261429","display_name":null,"funder_award_id":"326339","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"},{"id":"https://openalex.org/G4190525276","display_name":null,"funder_award_id":"326280","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"}],"funders":[{"id":"https://openalex.org/F4320310086","display_name":"Helsingin Yliopisto","ror":"https://ror.org/040af2s02"},{"id":"https://openalex.org/F4320321108","display_name":"Academy of Finland","ror":"https://ror.org/05k73zm37"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3129132077.pdf","grobid_xml":"https://content.openalex.org/works/W3129132077.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W3487859","https://openalex.org/W1554944419","https://openalex.org/W1557694976","https://openalex.org/W1730692908","https://openalex.org/W1775626196","https://openalex.org/W1805361780","https://openalex.org/W1965395441","https://openalex.org/W1967511636","https://openalex.org/W1999038366","https://openalex.org/W2006398000","https://openalex.org/W2022851810","https://openalex.org/W2040731319","https://openalex.org/W2066535939","https://openalex.org/W2073518255","https://openalex.org/W2099419573","https://openalex.org/W2145139979","https://openalex.org/W2158698691","https://openalex.org/W2186618285","https://openalex.org/W2289463038","https://openalex.org/W2528961511","https://openalex.org/W2556653490","https://openalex.org/W2605253252","https://openalex.org/W2787894218","https://openalex.org/W2880156893","https://openalex.org/W2894332792","https://openalex.org/W2911732975","https://openalex.org/W2912717607","https://openalex.org/W3102015031"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W4205140086","https://openalex.org/W2243388978","https://openalex.org/W4310746029","https://openalex.org/W4361981838","https://openalex.org/W2962755824","https://openalex.org/W2350969601","https://openalex.org/W2044774029"],"abstract_inverted_index":{"Abstract":[0],"Regression":[1],"analysis":[2],"is":[3,39,49,71,100,124],"a":[4,19,68,104],"standard":[5],"supervised":[6],"machine":[7],"learning":[8],"method":[9],"used":[10],"to":[11,37,61,90],"model":[12,69],"an":[13,78],"outcome":[14,33],"variable":[15,34],"in":[16,130,132],"terms":[17],"of":[18,21,31,86,93,107],"set":[20],"predictor":[22],"variables.":[23],"In":[24,73],"most":[25],"real-world":[26,134],"applications":[27],"the":[28,32,42,46,65,83,97,108],"true":[29],"value":[30],"we":[35,76],"want":[36],"predict":[38],"unknown":[40],"outside":[41],"training":[43],"data,":[44],"i.e.,":[45],"ground":[47,98],"truth":[48,99],"unknown.":[50,101],"Phenomena":[51],"such":[52],"as":[53],"overfitting":[54],"and":[55,110,115,123],"concept":[56,128],"drift":[57,129],"make":[58],"it":[59,120],"difficult":[60],"directly":[62],"observe":[63],"when":[64,96],"estimate":[66],"from":[67],"potentially":[70],"wrong.":[72],"this":[74],"paper":[75],"present":[77,103],"efficient":[79],"framework":[80,109],"for":[81,126],"estimating":[82],"generalization":[84],"error":[85],"regression":[87,94],"functions,":[88],"applicable":[89],"any":[91],"family":[92],"functions":[95],"We":[102,117],"theoretical":[105],"derivation":[106],"empirically":[111],"evaluate":[112],"its":[113],"strengths":[114],"limitations.":[116],"find":[118],"that":[119],"performs":[121],"robustly":[122],"useful":[125],"detecting":[127],"datasets":[131],"several":[133],"domains.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
