{"id":"https://openalex.org/W3158690762","doi":"https://doi.org/10.1145/3412841.3441922","title":"A new self-organizing map based algorithm for multi-label stream classification","display_name":"A new self-organizing map based algorithm for multi-label stream classification","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3158690762","doi":"https://doi.org/10.1145/3412841.3441922","mag":"3158690762"},"language":"en","primary_location":{"id":"doi:10.1145/3412841.3441922","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3441922","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM 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/A5066866262","display_name":"Ricardo Cerri","orcid":"https://orcid.org/0000-0002-2582-1695"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ricardo Cerri","raw_affiliation_strings":["Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073843750","display_name":"Joel David C.","orcid":null},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joel David C. Junior","raw_affiliation_strings":["Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Elaine. R. Faria","orcid":null},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Elaine. R. Faria","raw_affiliation_strings":["Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114376321","display_name":"Jo\u00e3o Gama","orcid":"https://orcid.org/0000-0003-3357-1195"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Jo\u00e3o Gama","raw_affiliation_strings":["University of Porto, Porto, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Porto, Porto, Portugal","institution_ids":["https://openalex.org/I182534213"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5597,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72515298,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"418","last_page":"426"},"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":0.9943000078201294,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9909999966621399,"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/concept-drift","display_name":"Concept drift","score":0.8591914176940918},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7921009063720703},{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.7736510038375854},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6457142233848572},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.6167349815368652},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5348514318466187},{"id":"https://openalex.org/keywords/self-organizing-map","display_name":"Self-organizing map","score":0.5165802240371704},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49719956517219543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48523926734924316},{"id":"https://openalex.org/keywords/online-algorithm","display_name":"Online algorithm","score":0.4311986565589905},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42836230993270874},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4183668792247772},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40136581659317017},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35758858919143677},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.2805917263031006},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.181447833776474}],"concepts":[{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.8591914176940918},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7921009063720703},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.7736510038375854},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6457142233848572},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.6167349815368652},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5348514318466187},{"id":"https://openalex.org/C111168008","wikidata":"https://www.wikidata.org/wiki/Q1136838","display_name":"Self-organizing map","level":3,"score":0.5165802240371704},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49719956517219543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48523926734924316},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.4311986565589905},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42836230993270874},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4183668792247772},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40136581659317017},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35758858919143677},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.2805917263031006},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.181447833776474},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3412841.3441922","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3441922","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","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":33,"referenced_works":["https://openalex.org/W54887220","https://openalex.org/W55768394","https://openalex.org/W1507852567","https://openalex.org/W1550946299","https://openalex.org/W1569512666","https://openalex.org/W1576514601","https://openalex.org/W1967542092","https://openalex.org/W2038624061","https://openalex.org/W2075721911","https://openalex.org/W2079810998","https://openalex.org/W2099419573","https://openalex.org/W2156935079","https://openalex.org/W2168701898","https://openalex.org/W2181764634","https://openalex.org/W2219140802","https://openalex.org/W2520368209","https://openalex.org/W2582292785","https://openalex.org/W2582743722","https://openalex.org/W2784762165","https://openalex.org/W2790955120","https://openalex.org/W2796702581","https://openalex.org/W2901072521","https://openalex.org/W2907669945","https://openalex.org/W2914580435","https://openalex.org/W2951829787","https://openalex.org/W3003253354","https://openalex.org/W3008214279","https://openalex.org/W3148494336","https://openalex.org/W4205616158","https://openalex.org/W4236769056","https://openalex.org/W4246988441","https://openalex.org/W4254034740","https://openalex.org/W4399521220"],"related_works":["https://openalex.org/W2240863542","https://openalex.org/W2576805520","https://openalex.org/W2109161140","https://openalex.org/W2352322083","https://openalex.org/W4312759464","https://openalex.org/W4287813662","https://openalex.org/W3017204156","https://openalex.org/W2323127059","https://openalex.org/W2003536922","https://openalex.org/W2375898159"],"abstract_inverted_index":{"Several":[0],"algorithms":[1,47],"have":[2],"been":[3],"proposed":[4],"for":[5,92,118],"offline":[6],"multi-label":[7,33,93],"classification.":[8],"However,":[9],"applications":[10],"in":[11,40,59,64,96,129,188,210],"areas":[12],"such":[13,54],"as":[14],"traffic":[15],"monitoring,":[16],"social":[17],"networks,":[18],"and":[19,127,160,172,182,194,213],"sensors":[20],"produce":[21],"data":[22,27,44],"continuously,":[23],"the":[24,36,41,71,75,104,154,161,169,173,179,186,189,208],"so":[25],"called":[26],"streams,":[28,45],"posing":[29],"challenges":[30],"to":[31,50,53,113,133,147],"batch":[32],"learning.":[34],"With":[35],"lack":[37],"of":[38,43,74,106,110,157,175,185],"stationarity":[39],"distribution":[42],"new":[46,135],"are":[48,78,124,166],"needed":[49],"online":[51,84,152],"adapt":[52,146],"changes":[55,62],"(concept":[56],"drift).":[57],"Also,":[58],"realistic":[60],"applications,":[61],"occur":[63],"scenarios":[65,97],"with":[66,98,204],"infinitely":[67,99],"delayed":[68,100],"labels,":[69],"where":[70],"true":[72],"classes":[73,140,187],"arrival":[76],"instances":[77,112],"never":[79,142],"available.":[80,144],"We":[81,102,145],"propose":[82],"an":[83,107,130],"unsupervised":[85],"incremental":[86,148],"method":[87,200],"based":[88],"on":[89],"self-organizing":[90,116],"maps":[91],"stream":[94,132],"classification":[95],"labels.":[101],"consider":[103],"existence":[105],"initial":[108],"set":[109],"labeled":[111],"train":[114],"a":[115],"map":[117],"each":[119,176],"label.":[120],"The":[121],"learned":[122],"models":[123],"then":[125],"used":[126],"adapted":[128],"evolving":[131],"classify":[134],"instances,":[136],"considering":[137],"that":[138,198],"their":[139],"will":[141],"be":[143],"concept":[149,214],"drifts":[150],"by":[151],"updating":[153],"weight":[155],"vectors":[156],"winner":[158],"neurons":[159],"dataset":[162],"label":[163],"cardinality.":[164],"Predictions":[165],"obtained":[167],"using":[168,192],"Bayes":[170],"rule":[171],"outputs":[174],"neuron,":[177],"adapting":[178],"prior":[180],"probabilities":[181,184],"conditional":[183],"stream.":[190],"Experiments":[191],"synthetic":[193],"real":[195],"datasets":[196],"show":[197],"our":[199],"is":[201],"highly":[202],"competitive":[203],"several":[205],"ones":[206],"from":[207],"literature,":[209],"both":[211],"stationary":[212],"drift":[215],"scenarios.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}