{"id":"https://openalex.org/W2777138789","doi":"https://doi.org/10.1007/s10994-017-5686-9","title":"The online performance estimation framework: heterogeneous ensemble learning for data streams","display_name":"The online performance estimation framework: heterogeneous ensemble learning for data streams","publication_year":2017,"publication_date":"2017-12-21","ids":{"openalex":"https://openalex.org/W2777138789","doi":"https://doi.org/10.1007/s10994-017-5686-9","mag":"2777138789"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-017-5686-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-017-5686-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-017-5686-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","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/s10994-017-5686-9.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009236758","display_name":"Jan N. van Rijn","orcid":"https://orcid.org/0000-0003-2898-2168"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]},{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE","NL"],"is_corresponding":true,"raw_author_name":"Jan N. van Rijn","raw_affiliation_strings":["Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands","University of Freiburg, Freiburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]},{"raw_affiliation_string":"University of Freiburg, Freiburg, Germany","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063502807","display_name":"Geoffrey Holmes","orcid":"https://orcid.org/0000-0003-0433-8925"},"institutions":[{"id":"https://openalex.org/I52179390","display_name":"University of Waikato","ror":"https://ror.org/013fsnh78","country_code":"NZ","type":"education","lineage":["https://openalex.org/I52179390"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Geoffrey Holmes","raw_affiliation_strings":["University of Waikato, Hamilton, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waikato, Hamilton, New Zealand","institution_ids":["https://openalex.org/I52179390"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087785022","display_name":"Bernhard Pfahringer","orcid":"https://orcid.org/0000-0002-3732-5787"},"institutions":[{"id":"https://openalex.org/I52179390","display_name":"University of Waikato","ror":"https://ror.org/013fsnh78","country_code":"NZ","type":"education","lineage":["https://openalex.org/I52179390"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Bernhard Pfahringer","raw_affiliation_strings":["University of Waikato, Hamilton, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waikato, Hamilton, New Zealand","institution_ids":["https://openalex.org/I52179390"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016794035","display_name":"Joaquin Vanschoren","orcid":"https://orcid.org/0000-0001-7044-9805"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Joaquin Vanschoren","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009236758"],"corresponding_institution_ids":["https://openalex.org/I121797337","https://openalex.org/I161046081"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":8.601,"has_fulltext":true,"cited_by_count":131,"citation_normalized_percentile":{"value":0.98080798,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"107","issue":"1","first_page":"149","last_page":"176"},"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":0.9911999702453613,"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":0.9835000038146973,"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":0.8290730714797974},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.7357335686683655},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.7041899561882019},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6834615468978882},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6591365337371826},{"id":"https://openalex.org/keywords/cascading-classifiers","display_name":"Cascading classifiers","score":0.5840228199958801},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5747411847114563},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.5704026222229004},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.5630893707275391},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.542235791683197},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.5289506912231445},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.508223295211792},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4854934513568878},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46402719616889954},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4141228199005127}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8290730714797974},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7357335686683655},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.7041899561882019},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6834615468978882},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6591365337371826},{"id":"https://openalex.org/C40651066","wikidata":"https://www.wikidata.org/wiki/Q5048220","display_name":"Cascading classifiers","level":4,"score":0.5840228199958801},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5747411847114563},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.5704026222229004},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.5630893707275391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.542235791683197},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.5289506912231445},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.508223295211792},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4854934513568878},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46402719616889954},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4141228199005127},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":7,"locations":[{"id":"doi:10.1007/s10994-017-5686-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-017-5686-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-017-5686-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:pure.tue.nl:openaire/7337bd44-8704-44bd-a605-df43a6597dfc","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/7337bd44-8704-44bd-a605-df43a6597dfc","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"van Rijn, J N, Holmes, G, Pfahringer, B & Vanschoren, J 2018, 'The online performance estimation framework: heterogeneous ensemble learning for data streams', Machine Learning, vol. 107, no. 1, pp. 149\u2013176. https://doi.org/10.1007/s10994-017-5686-9","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:researchcommons.waikato.ac.nz:10289/12906","is_oa":true,"landing_page_url":"https://hdl.handle.net/10289/12906","pdf_url":"https://hdl.handle.net/10289/12906","source":{"id":"https://openalex.org/S4306400944","display_name":"Research Commons (University of Waikato)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I52179390","host_organization_name":"University of Waikato","host_organization_lineage":["https://openalex.org/I52179390"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"},{"id":"pmh:oai:pure.tue.nl:publications/7337bd44-8704-44bd-a605-df43a6597dfc","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85038615817&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"van Rijn, J N, Holmes, G, Pfahringer, B & Vanschoren, J 2018, 'The online performance estimation framework: heterogeneous ensemble learning for data streams', Machine Learning, vol. 107, no. 1, pp. 149\u2013176. https://doi.org/10.1007/s10994-017-5686-9","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:scholarlypublications.universiteitleiden.nl:item_2945446","is_oa":false,"landing_page_url":"https://hdl.handle.net/1887/61062","pdf_url":null,"source":{"id":"https://openalex.org/S4306400850","display_name":"Leiden Repository (Leiden University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I121797337","host_organization_name":"Leiden University","host_organization_lineage":["https://openalex.org/I121797337"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning","raw_type":"Article / Letter to editor"},{"id":"pmh:tue:oai:pure.tue.nl:publications/7337bd44-8704-44bd-a605-df43a6597dfc","is_oa":true,"landing_page_url":"https://research.tue.nl/nl/publications/7337bd44-8704-44bd-a605-df43a6597dfc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning, 107(1). Springer","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:ul:oai:scholarlypublications.universiteitleiden.nl:item_2945446","is_oa":true,"landing_page_url":"http://hdl.handle.net/1887/61062","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning, 107(1), 149 - 176","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s10994-017-5686-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-017-5686-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-017-5686-9.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4027227427","display_name":null,"funder_award_id":"612.001.206","funder_id":"https://openalex.org/F4320334893","funder_display_name":"Stichting voor de Technische Wetenschappen"},{"id":"https://openalex.org/G6246610806","display_name":null,"funder_award_id":"Emmy Noether grant HU 1900/2-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320334893","display_name":"Stichting voor de Technische Wetenschappen","ror":"https://ror.org/057tq3593"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2777138789.pdf","grobid_xml":"https://content.openalex.org/works/W2777138789.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W34738725","https://openalex.org/W122993283","https://openalex.org/W148859753","https://openalex.org/W1495775210","https://openalex.org/W1516923404","https://openalex.org/W1517567737","https://openalex.org/W1525647652","https://openalex.org/W1529840045","https://openalex.org/W1565746575","https://openalex.org/W1585854823","https://openalex.org/W1769098366","https://openalex.org/W1824270332","https://openalex.org/W1935278564","https://openalex.org/W1969574535","https://openalex.org/W1980264541","https://openalex.org/W1985157189","https://openalex.org/W1990990210","https://openalex.org/W2002830978","https://openalex.org/W2009727399","https://openalex.org/W2032196926","https://openalex.org/W2042204882","https://openalex.org/W2068714596","https://openalex.org/W2073256825","https://openalex.org/W2080581973","https://openalex.org/W2083144552","https://openalex.org/W2084689094","https://openalex.org/W2093717447","https://openalex.org/W2093825590","https://openalex.org/W2116881978","https://openalex.org/W2123528125","https://openalex.org/W2125993116","https://openalex.org/W2132862423","https://openalex.org/W2133990480","https://openalex.org/W2134125037","https://openalex.org/W2135293965","https://openalex.org/W2135335717","https://openalex.org/W2143991132","https://openalex.org/W2167467747","https://openalex.org/W2171809276","https://openalex.org/W2245178135","https://openalex.org/W2575220271","https://openalex.org/W2912573428","https://openalex.org/W2912934387","https://openalex.org/W4237640996","https://openalex.org/W4237961478","https://openalex.org/W4250042253","https://openalex.org/W6658263355","https://openalex.org/W6661953458","https://openalex.org/W6680192438","https://openalex.org/W6680901108","https://openalex.org/W6808111085"],"related_works":["https://openalex.org/W2743555903","https://openalex.org/W1995624305","https://openalex.org/W2083259201","https://openalex.org/W2137567908","https://openalex.org/W1940098242","https://openalex.org/W2167825284","https://openalex.org/W88722211","https://openalex.org/W4387081027","https://openalex.org/W2534614934","https://openalex.org/W3167703153"],"abstract_inverted_index":{"Ensembles":[0],"of":[1,18,73,94,105,123,156,166],"classifiers":[2,8,27,125],"are":[3,28,33,54,187],"among":[4,63],"the":[5,16,51,61,64,84,103,113,121,167],"best":[6],"performing":[7],"available":[9,192],"in":[10,126],"many":[11],"data":[12,19,86,109,157],"mining":[13,17],"applications,":[14],"including":[15,171],"streams.":[20,110],"Rather":[21],"than":[22],"training":[23,135],"one":[24,92],"classifier,":[25,96],"multiple":[26],"trained,":[29],"and":[30,146,174,190],"their":[31,149],"predictions":[32],"combined":[34],"according":[35],"to":[36,46,58,67],"a":[37,153],"given":[38],"voting":[39],"schedule.":[40],"An":[41],"important":[42],"prerequisite":[43],"for":[44,83,108],"ensembles":[45,80,107],"be":[47],"successful":[48],"is":[49,66,162],"that":[50,161],"individual":[52,124],"models":[53,65],"diverse.":[55],"One":[56],"way":[57],"vastly":[59],"increase":[60],"diversity":[62],"build":[68],"an":[69,127,130],"heterogeneous":[70,106],"ensemble,":[71],"comprised":[72],"fundamentally":[74],"different":[75],"model":[76],"types.":[77],"However,":[78],"most":[79,97],"developed":[81],"specifically":[82],"dynamic":[85],"stream":[87],"setting":[88],"rely":[89],"on":[90,133,144],"only":[91],"type":[93],"base-level":[95],"often":[98],"Hoeffding":[99],"Trees.":[100],"We":[101,111],"study":[102],"use":[104],"introduce":[112],"Online":[114,172],"Performance":[115],"Estimation":[116],"framework,":[117],"which":[118],"dynamically":[119,147],"weights":[120],"votes":[122],"ensemble.":[128],"Using":[129],"internal":[131],"evaluation":[132],"recent":[134],"data,":[136],"it":[137],"measures":[138],"how":[139],"well":[140],"ensemble":[141,169],"members":[142],"performed":[143],"this":[145,185],"updates":[148],"weights.":[150],"Experiments":[151],"over":[152],"wide":[154],"range":[155],"streams":[158],"show":[159],"performance":[160],"competitive":[163],"with":[164],"state":[165],"art":[168],"techniques,":[170],"Bagging":[173],"Leveraging":[175],"Bagging,":[176],"while":[177],"being":[178],"significantly":[179],"faster.":[180],"All":[181],"experimental":[182],"results":[183],"from":[184],"work":[186],"easily":[188],"reproducible":[189],"publicly":[191],"online.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":34},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":5}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
