{"id":"https://openalex.org/W4312685616","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892439","title":"Measuring Drift Severity by Tree Structure Classifiers","display_name":"Measuring Drift Severity by Tree Structure Classifiers","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312685616","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892439"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892439","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 International Joint Conference on Neural Networks (IJCNN)","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/A5025337002","display_name":"Di Zhao","orcid":"https://orcid.org/0000-0002-8575-0294"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Di Zhao","raw_affiliation_strings":["School of Computer Science, The University of Auckland,Auckland,New Zealand","School of Computer Science, The University of Auckland, Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Auckland,Auckland,New Zealand","institution_ids":["https://openalex.org/I154130895"]},{"raw_affiliation_string":"School of Computer Science, The University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017570709","display_name":"Yun Sing Koh","orcid":"https://orcid.org/0000-0001-7256-4049"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Yun Sing Koh","raw_affiliation_strings":["School of Computer Science, The University of Auckland,Auckland,New Zealand","School of Computer Science, The University of Auckland, Auckland, New Zealand"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Auckland,Auckland,New Zealand","institution_ids":["https://openalex.org/I154130895"]},{"raw_affiliation_string":"School of Computer Science, The University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038682039","display_name":"Philippe Fournier\u2010Viger","orcid":"https://orcid.org/0000-0002-7680-9899"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Philippe Fournier-Viger","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China","College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University,Shenzhen,China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5025337002"],"corresponding_institution_ids":["https://openalex.org/I154130895"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15632407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.9739999771118164,"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/T11407","display_name":"Innovative Microfluidic and Catalytic Techniques Innovation","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.9678783416748047},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.70633465051651},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6451941132545471},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5895225405693054},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.5744870901107788},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5585741400718689},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.5335759520530701},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4924079179763794},{"id":"https://openalex.org/keywords/binary-tree","display_name":"Binary tree","score":0.431557297706604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4217739403247833},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38368460536003113},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.3508074879646301},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20503586530685425},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15493783354759216}],"concepts":[{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.9678783416748047},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.70633465051651},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6451941132545471},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5895225405693054},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.5744870901107788},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5585741400718689},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.5335759520530701},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4924079179763794},{"id":"https://openalex.org/C197855036","wikidata":"https://www.wikidata.org/wiki/Q380172","display_name":"Binary tree","level":2,"score":0.431557297706604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4217739403247833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38368460536003113},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.3508074879646301},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20503586530685425},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15493783354759216},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892439","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 International Joint Conference on Neural Networks (IJCNN)","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":36,"referenced_works":["https://openalex.org/W27170557","https://openalex.org/W109131977","https://openalex.org/W129802993","https://openalex.org/W1585854823","https://openalex.org/W1978478796","https://openalex.org/W1990079212","https://openalex.org/W2010657328","https://openalex.org/W2051903196","https://openalex.org/W2097178527","https://openalex.org/W2100406636","https://openalex.org/W2120587290","https://openalex.org/W2140164381","https://openalex.org/W2145898068","https://openalex.org/W2167845177","https://openalex.org/W2308071406","https://openalex.org/W2552178666","https://openalex.org/W2587703207","https://openalex.org/W2747716660","https://openalex.org/W2898017895","https://openalex.org/W2991979044","https://openalex.org/W3086133400","https://openalex.org/W3095110363","https://openalex.org/W3120740533","https://openalex.org/W3161172664","https://openalex.org/W3167537398","https://openalex.org/W3175350659","https://openalex.org/W3177490101","https://openalex.org/W3196735466","https://openalex.org/W4255466416","https://openalex.org/W6605327130","https://openalex.org/W6635179022","https://openalex.org/W6680844135","https://openalex.org/W6681814599","https://openalex.org/W6698240980","https://openalex.org/W6729885558","https://openalex.org/W6783128441"],"related_works":["https://openalex.org/W2802243998","https://openalex.org/W4281572076","https://openalex.org/W2469699777","https://openalex.org/W2060628068","https://openalex.org/W2773951400","https://openalex.org/W3013371665","https://openalex.org/W4391093024","https://openalex.org/W3208495060","https://openalex.org/W2277307313","https://openalex.org/W2235038291"],"abstract_inverted_index":{"Streaming":[0],"data":[1,11,21,74],"has":[2],"become":[3],"more":[4],"common":[5],"as":[6],"our":[7],"ability":[8],"to":[9,149],"collect":[10],"in":[12,18,29,50,65],"real-time":[13],"increases.":[14],"A":[15,163],"primary":[16],"concern":[17],"dealing":[19],"with":[20],"streams":[22],"is":[23,39],"concept":[24,52],"drift,":[25,97],"which":[26],"describes":[27],"changes":[28,64],"the":[30,63,66,71,81,85,128,150,155,168,183],"underlying":[31],"distribution":[32],"of":[33,138],"streaming":[34],"data.":[35,125],"Measuring":[36],"drift":[37,53,59,82,121,178,184],"severity":[38,45,60,83],"crucial":[40],"for":[41],"model":[42],"adaptation.":[43],"Drift":[44,112],"can":[46,172],"be":[47,173],"a":[48,107],"proxy":[49],"choosing":[51],"adaptation":[54,179],"strategies.":[55],"Current":[56],"methods":[57,78,93,100,152],"measure":[58,80,95,102],"by":[61,133,175],"monitoring":[62],"learner":[67,170],"performance":[68,148,171],"or":[69],"measuring":[70],"difference":[72,129],"between":[73,130,158],"distributions.":[75],"However,":[76],"these":[77],"cannot":[79,94,101],"if":[84],"ground":[86],"truth":[87],"labels":[88],"are":[89],"unavailable.":[90],"Specifically,":[91],"performance-based":[92],"marginal":[96,118],"and":[98,119,153,160],"distribution-based":[99],"conditional":[103,120],"drift.":[104],"We":[105],"propose":[106],"novel":[108],"framework":[109],"named":[110],"Tree-based":[111],"Measurement":[113],"(TDM)":[114],"that":[115,144,167],"measures":[116,127],"both":[117],"without":[122],"revisiting":[123],"historical":[124],"TDM":[126,145],"tree":[131],"classifiers":[132],"transforming":[134],"them":[135],"into":[136],"sets":[137],"binary":[139],"vectors.":[140],"An":[141],"experiment":[142],"shows":[143,166],"achieves":[146],"similar":[147],"state-of-the-art":[151],"provides":[154],"best":[156],"trade-off":[157],"runtime":[159],"memory":[161],"usage.":[162],"case":[164],"study":[165],"online":[169],"improved":[174],"adapting":[176],"different":[177],"strategies":[180],"based":[181],"on":[182],"severity.":[185]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
