{"id":"https://openalex.org/W1995624305","doi":"https://doi.org/10.1109/cidm.2013.6597237","title":"SAE: Social Adaptive Ensemble classifier for data streams","display_name":"SAE: Social Adaptive Ensemble classifier for data streams","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W1995624305","doi":"https://doi.org/10.1109/cidm.2013.6597237","mag":"1995624305"},"language":"en","primary_location":{"id":"doi:10.1109/cidm.2013.6597237","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cidm.2013.6597237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","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/A5071380196","display_name":"Heitor Murilo Gomes","orcid":"https://orcid.org/0000-0002-5276-637X"},"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":"Heitor Murilo Gomes","raw_affiliation_strings":["Programa de P\u00f3s-Gradua\u00e7\u00e3o em Inform\u00e1tica, Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1, Curitiba, Brazil","Programa de Pos-Grad. em Inf. (PPGIA), Pontificia Univ. Catolica do Parana (PUCPR), Curitiba, Brazil"],"affiliations":[{"raw_affiliation_string":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Inform\u00e1tica, Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1, Curitiba, Brazil","institution_ids":["https://openalex.org/I176838256"]},{"raw_affiliation_string":"Programa de Pos-Grad. em Inf. (PPGIA), Pontificia Univ. Catolica do Parana (PUCPR), 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":"Fabricio Enembreck","raw_affiliation_strings":["Programa de P\u00f3s-Gradua\u00e7\u00e3o em Inform\u00e1tica, Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1, Curitiba, Brazil","Programa de Pos-Grad. em Inf. (PPGIA), Pontificia Univ. Catolica do Parana (PUCPR), Curitiba, Brazil"],"affiliations":[{"raw_affiliation_string":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Inform\u00e1tica, Pontif\u00edcia Universidade Cat\u00f3lica do Paran\u00e1, Curitiba, Brazil","institution_ids":["https://openalex.org/I176838256"]},{"raw_affiliation_string":"Programa de Pos-Grad. em Inf. (PPGIA), Pontificia Univ. Catolica do Parana (PUCPR), Curitiba, Brazil","institution_ids":["https://openalex.org/I176838256"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071380196"],"corresponding_institution_ids":["https://openalex.org/I176838256"],"apc_list":null,"apc_paid":null,"fwci":1.9237,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.87770779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"199","last_page":"206"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9690999984741211,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9496999979019165,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.8396089673042297},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8162357807159424},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7477408051490784},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.7456308603286743},{"id":"https://openalex.org/keywords/cascading-classifiers","display_name":"Cascading classifiers","score":0.6870434284210205},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6109402775764465},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6080014109611511},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5711479187011719},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.5655897855758667},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.5612812638282776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.528558611869812},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46499931812286377},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4313257038593292}],"concepts":[{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.8396089673042297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8162357807159424},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7477408051490784},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.7456308603286743},{"id":"https://openalex.org/C40651066","wikidata":"https://www.wikidata.org/wiki/Q5048220","display_name":"Cascading classifiers","level":4,"score":0.6870434284210205},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6109402775764465},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6080014109611511},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5711479187011719},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.5655897855758667},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.5612812638282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.528558611869812},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46499931812286377},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4313257038593292},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cidm.2013.6597237","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cidm.2013.6597237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1529840045","https://openalex.org/W1990079212","https://openalex.org/W2016159616","https://openalex.org/W2068714596","https://openalex.org/W2084689094","https://openalex.org/W2100406636","https://openalex.org/W2110222014","https://openalex.org/W2124868070","https://openalex.org/W2143991132","https://openalex.org/W2167917621","https://openalex.org/W2361024996","https://openalex.org/W2400267228","https://openalex.org/W2487087946","https://openalex.org/W2912934387","https://openalex.org/W4212883601"],"related_works":["https://openalex.org/W2940903377","https://openalex.org/W3108897387","https://openalex.org/W4229924696","https://openalex.org/W4386121812","https://openalex.org/W4307392573","https://openalex.org/W2912132049","https://openalex.org/W2186919162","https://openalex.org/W1995624305","https://openalex.org/W2167825284","https://openalex.org/W2368143139"],"abstract_inverted_index":{"This":[0,199],"work":[1],"encompasses":[2],"the":[3,21,27,39,60,66,92,159],"development":[4],"of":[5,29,38,68,94,130,239],"a":[6,12,69,119,144,226,256],"new":[7],"ensemble":[8,61,78,116],"classifier":[9],"that":[10,123,147,223],"uses":[11],"Social":[13,22],"Network":[14],"abstraction":[15],"for":[16,229],"Data":[17],"Stream":[18],"Classification,":[19],"namely":[20],"Adaptive":[23],"Ensemble":[24,48],"(SAE).":[25],"In":[26],"context":[28],"data":[30,55,105,214,234],"stream":[31,230,235],"classification,":[32,231],"concept":[33,70,175],"drift":[34,221],"is":[35,112,190,225],"considered":[36],"one":[37],"most":[40],"difficult":[41],"and":[42,85,88,154,195,204,219,245,252],"important":[43],"issues":[44],"to":[45,54,114,126,136,142,169,181,193],"be":[46,51],"addressed.":[47],"classifiers":[49,98,111,139,153,172,189],"can":[50],"successfully":[52],"applied":[53],"streams":[56,215],"as":[57,59,81,249],"long":[58],"efficiently":[62],"adapts":[63],"itself":[64],"in":[65,118],"occurrence":[67],"drift.":[71],"SAE":[72,224],"algorithm":[73],"inherits":[74],"strategies":[75],"from":[76],"other":[77,183],"methods,":[79],"such":[80,248],"Online":[82],"Bagging":[83],"[4]":[84],"DWM":[86],"[2],":[87],"merge":[89],"these":[90,157,178],"with":[91,212],"notion":[93],"connectivity":[95],"between":[96,110,188],"similar":[97,132,138,152],"w.r.t.":[99],"their":[100],"individual":[101],"predictions.":[102],"The":[103,186],"relational":[104],"obtained":[106],"through":[107],"measuring":[108],"similarities":[109],"used":[113,192],"arrange":[115],"members":[117],"social":[120],"network":[121],"structure":[122],"allows":[124,140],"us":[125,141],"identify":[127,137,194],"subgroups":[128],"(subnetworks)":[129],"highly":[131],"classifiers.":[133,185,198],"Being":[134],"able":[135],"implement":[143],"combination":[145,164],"strategy":[146,165],"first":[148],"combines":[149,156],"predictions":[150,173],"within":[151],"later":[155],"into":[158],"final":[160],"prediction.":[161],"Moreover,":[162],"this":[163],"assigns":[166],"more":[167],"weight":[168],"recently":[170],"added":[171],"during":[174],"drifts,":[176],"since":[177],"are":[179,242,255],"dissimilar":[180],"all":[182],"existing":[184],"similarity":[187],"also":[191],"remove":[196],"redundant":[197],"effectively":[200],"saves":[201],"systems":[202],"resources":[203],"sometimes":[205],"improves":[206],"accuracy.":[207],"We":[208],"present":[209],"empirical":[210],"experiments":[211],"synthetic":[213],"containing":[216],"abrupt,":[217],"gradual":[218],"no":[220],"showing":[222],"valid":[227],"option":[228],"especially":[232],"when":[233],"characteristics":[236],"(e.g.":[237],"presence":[238],"abrupt":[240],"drifts)":[241],"previously":[243],"unknown":[244],"system":[246],"resources,":[247],"CPU":[250],"time":[251],"memory":[253],"space,":[254],"concern.":[257]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}