{"id":"https://openalex.org/W2943801183","doi":"https://doi.org/10.1145/3297280.3297334","title":"Learning regularized hoeffding trees from data streams","display_name":"Learning regularized hoeffding trees from data streams","publication_year":2019,"publication_date":"2019-04-08","ids":{"openalex":"https://openalex.org/W2943801183","doi":"https://doi.org/10.1145/3297280.3297334","mag":"2943801183"},"language":"en","primary_location":{"id":"doi:10.1145/3297280.3297334","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297280.3297334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing","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/A5071288743","display_name":"Jean Paul Barddal","orcid":"https://orcid.org/0000-0001-9928-854X"},"institutions":[{"id":"https://openalex.org/I176838256","display_name":"Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1","ror":"https://ror.org/02x1vjk79","country_code":"BR","type":"education","lineage":["https://openalex.org/I176838256"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Jean Paul Barddal","raw_affiliation_strings":["Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1 (PUC-PR), Curitiba, Brazil"],"affiliations":[{"raw_affiliation_string":"Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1 (PUC-PR), Curitiba, Brazil","institution_ids":["https://openalex.org/I176838256"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035426257","display_name":"Fabr\u00edcio Enembreck","orcid":"https://orcid.org/0000-0002-1418-3245"},"institutions":[{"id":"https://openalex.org/I176838256","display_name":"Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1","ror":"https://ror.org/02x1vjk79","country_code":"BR","type":"education","lineage":["https://openalex.org/I176838256"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Fabr\u00edcio Enembreck","raw_affiliation_strings":["Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1 (PUC-PR), Curitiba, Brazil"],"affiliations":[{"raw_affiliation_string":"Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1 (PUC-PR), Curitiba, Brazil","institution_ids":["https://openalex.org/I176838256"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071288743"],"corresponding_institution_ids":["https://openalex.org/I176838256"],"apc_list":null,"apc_paid":null,"fwci":1.1573,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.84002101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"574","last_page":"581"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9887999892234802,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9850000143051147,"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/data-stream-mining","display_name":"Data stream mining","score":0.7047871351242065},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.665877103805542},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.663253903388977},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5027472972869873},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48908907175064087},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.46992385387420654},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4504578411579132},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4416744112968445},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.4370863437652588},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.33545631170272827},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31711041927337646},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18722277879714966}],"concepts":[{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.7047871351242065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.665877103805542},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.663253903388977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5027472972869873},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48908907175064087},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.46992385387420654},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4504578411579132},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4416744112968445},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.4370863437652588},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33545631170272827},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31711041927337646},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18722277879714966},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3297280.3297334","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297280.3297334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1525647652","https://openalex.org/W1565746575","https://openalex.org/W1935278564","https://openalex.org/W1990079212","https://openalex.org/W2010657328","https://openalex.org/W2015627422","https://openalex.org/W2022775778","https://openalex.org/W2032196926","https://openalex.org/W2068714596","https://openalex.org/W2084689094","https://openalex.org/W2100406636","https://openalex.org/W2102098892","https://openalex.org/W2134125037","https://openalex.org/W2135046866","https://openalex.org/W2143991132","https://openalex.org/W2155925556","https://openalex.org/W2230553583","https://openalex.org/W2293050121","https://openalex.org/W2467405173","https://openalex.org/W2517845378","https://openalex.org/W2791425844","https://openalex.org/W2949979142","https://openalex.org/W2997546679","https://openalex.org/W3003253354","https://openalex.org/W4385486090"],"related_works":["https://openalex.org/W2802243998","https://openalex.org/W4281572076","https://openalex.org/W2469699777","https://openalex.org/W2060628068","https://openalex.org/W3013371665","https://openalex.org/W2773951400","https://openalex.org/W4391093024","https://openalex.org/W3208495060","https://openalex.org/W2277307313","https://openalex.org/W2235038291"],"abstract_inverted_index":{"Learning":[0],"from":[1,57,157,209],"data":[2,21,77,196],"streams":[3],"is":[4,178],"a":[5,37,120,129,139,144,172,218],"hot":[6],"topic":[7],"in":[8,36,71,160,226],"machine":[9],"learning":[10,14,54],"that":[11,126,146,198],"targets":[12],"the":[13,46,61,92,106,134,161,167,199,221,240,243],"and":[15,27,33,88,111,153,183,193,205,231],"update":[16],"of":[17,39,51,73,186,220,242],"predictive":[18],"models":[19],"as":[20,75],"becomes":[22,78],"available":[23],"for":[24,53,123],"both":[25,181,203],"training":[26],"query.":[28],"Due":[29],"to":[30,68,97,108,132,164],"their":[31],"simplicity":[32],"convincing":[34],"results":[35],"multitude":[38],"applications,":[40],"Hoeffding":[41,65,103,124,187,207],"Trees":[42,66,104,125,208],"are,":[43],"by":[44,137],"far,":[45],"most":[47],"widely":[48],"used":[49,150],"family":[50],"methods":[52],"decision":[55],"trees":[56],"streaming":[58],"data.":[59],"Despite":[60],"aforementioned":[62],"positive":[63],"characteristics,":[64],"tend":[67],"continuously":[69],"grow":[70],"terms":[72],"nodes":[74],"new":[76,140,173],"available,":[79,87],"i.e.,":[80],"they":[81],"eventually":[82],"split":[83,141],"on":[84,91],"all":[85],"features":[86],"multiple":[89],"times":[90],"same":[93],"feature;":[94],"thus":[95,151,238],"leading":[96],"unnecessary":[98],"complexity.":[99],"With":[100],"this":[101],"behavior,":[102],"lose":[105],"ability":[107],"be":[109],"human-understandable":[110],"computationally":[112],"efficient.":[113],"To":[114],"tackle":[115],"these":[116],"issues,":[117],"we":[118],"propose":[119],"regularization":[121,222,245],"scheme":[122,177],"(i)":[127],"uses":[128,155],"penalty":[130],"factor":[131],"control":[133],"gain":[135,168],"obtained":[136],"creating":[138],"node":[142],"using":[143,190],"feature":[145],"has":[147],"not":[148],"been":[149,236],"far;":[152],"(ii)":[154],"information":[156],"previous":[158],"splits":[159],"current":[162],"branch":[163],"determine":[165],"whether":[166],"observed":[169],"indeed":[170],"justifies":[171],"split.":[174],"The":[175],"proposed":[176,200,244],"combined":[179],"with":[180],"standard":[182],"adaptive":[184,206],"variants":[185],"Trees.":[188],"Experiments":[189],"real-world,":[191],"stationary":[192],"drifting":[194],"synthetic":[195],"show":[197],"method":[201],"prevents":[202],"original":[204],"unnecessarily":[210],"growing":[211],"while":[212],"maintaining":[213],"impressive":[214],"accuracy":[215],"rates.":[216],"As":[217],"byproduct":[219],"process,":[223],"significant":[224],"improvements":[225],"processing":[227],"time,":[228],"model":[229],"complexity,":[230],"memory":[232],"consumption":[233],"have":[234],"also":[235],"observed,":[237],"showing":[239],"effectiveness":[241],"scheme.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":6}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}