{"id":"https://openalex.org/W4207003356","doi":"https://doi.org/10.1109/ssci50451.2021.9660035","title":"Naive Importance Weighting for Data Stream with Intermediate Latency","display_name":"Naive Importance Weighting for Data Stream with Intermediate Latency","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4207003356","doi":"https://doi.org/10.1109/ssci50451.2021.9660035"},"language":"en","primary_location":{"id":"doi:10.1109/ssci50451.2021.9660035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci50451.2021.9660035","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","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":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5049207762","display_name":"Pedro Henrique Parreira","orcid":null},"institutions":[{"id":"https://openalex.org/I71715416","display_name":"Universidade Federal do ABC","ror":"https://ror.org/028kg9j04","country_code":"BR","type":"education","lineage":["https://openalex.org/I71715416"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Pedro Henrique Parreira","raw_affiliation_strings":["Federal University of ABC (UFABC), Santo Andre, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of ABC (UFABC), Santo Andre, Brazil","institution_ids":["https://openalex.org/I71715416"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005717519","display_name":"Ronaldo C. Prati","orcid":"https://orcid.org/0000-0001-8597-4987"},"institutions":[{"id":"https://openalex.org/I71715416","display_name":"Universidade Federal do ABC","ror":"https://ror.org/028kg9j04","country_code":"BR","type":"education","lineage":["https://openalex.org/I71715416"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ronaldo Cristiano Prati","raw_affiliation_strings":["Federal University of ABC (UFABC), Santo Andre, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of ABC (UFABC), Santo Andre, Brazil","institution_ids":["https://openalex.org/I71715416"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049207762"],"corresponding_institution_ids":["https://openalex.org/I71715416"],"apc_list":null,"apc_paid":null,"fwci":0.1257,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.40689849,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"7"},"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":0.9927999973297119,"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":0.9828000068664551,"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":0.8589714765548706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7708792686462402},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.760729968547821},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.748752772808075},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.628210723400116},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5134926438331604},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.40593090653419495},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3577228784561157},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34927845001220703}],"concepts":[{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.8589714765548706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7708792686462402},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.760729968547821},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.748752772808075},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.628210723400116},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5134926438331604},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.40593090653419495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3577228784561157},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34927845001220703},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci50451.2021.9660035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci50451.2021.9660035","pdf_url":null,"source":{"id":"https://openalex.org/S4363604921","display_name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","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":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","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":26,"referenced_works":["https://openalex.org/W91088564","https://openalex.org/W1885048467","https://openalex.org/W2015627422","https://openalex.org/W2045938006","https://openalex.org/W2099419573","https://openalex.org/W2101895600","https://openalex.org/W2120708938","https://openalex.org/W2139138573","https://openalex.org/W2157664934","https://openalex.org/W2165698076","https://openalex.org/W2434851943","https://openalex.org/W2583824097","https://openalex.org/W2847284300","https://openalex.org/W2884253456","https://openalex.org/W2905241037","https://openalex.org/W2979509742","https://openalex.org/W2989114215","https://openalex.org/W2997010842","https://openalex.org/W2997967197","https://openalex.org/W3010768238","https://openalex.org/W3090554858","https://openalex.org/W3095459387","https://openalex.org/W3111828510","https://openalex.org/W4299451673","https://openalex.org/W6662096809","https://openalex.org/W6752866118"],"related_works":["https://openalex.org/W4307392573","https://openalex.org/W2802243998","https://openalex.org/W2736127210","https://openalex.org/W2329342202","https://openalex.org/W2574092225","https://openalex.org/W2161835057","https://openalex.org/W1521014365","https://openalex.org/W4200217704","https://openalex.org/W3208495060","https://openalex.org/W2740428142"],"abstract_inverted_index":{"In":[0,175],"the":[1,21,27,43,52,59,64,90,93,138,147,160,185,234,237],"literature,":[2],"it":[3,48],"is":[4,14,38,77,86,109,208,226],"often":[5],"assumed":[6],"that":[7,182,223],"given":[8],"a":[9,114,118,126,171,180,188,192,199],"data":[10,94,101,119,193],"stream,":[11],"every":[12],"instance":[13,149],"provided":[15],"with":[16,71,83,121,131,195,217,230],"its":[17],"correct":[18],"label":[19,54,66],"after":[20,61],"prediction":[22],"for":[23,155,214],"performance":[24,186],"evaluation":[25],"(i.e.,":[26],"null":[28],"latency":[29,85,145,197,249],"scenario).":[30],"However,":[31,141],"in":[32,42,89,117,142,191,228,244],"several":[33],"real":[34,235],"applications,":[35],"this":[36,176,231],"assumption":[37],"invalid.":[39],"For":[40,233],"example,":[41],"problem":[44],"of":[45,128,167,173,187,246],"predicting":[46],"if":[47],"will":[49,55,67,124,163],"rain":[50],"tomorrow,":[51,62],"actual":[53,65],"only":[56],"be":[57,68,97,153,165],"available":[58,70],"day":[60],"i.e.,":[63,99],"made":[69],"an":[72,143],"unavoidable":[73],"delay.":[74],"This":[75,107],"scenario":[76],"called":[78,110,203,209],"intermediate":[79,84,144,196,248],"latency.":[80],"Unfortunately,":[81],"learning":[82],"little":[87],"investigated":[88],"literature.":[91],"Furthermore,":[92],"stream":[95,120,194],"can":[96,103,183],"non-stationary,":[98],"their":[100,132],"distribution":[102],"change":[104],"over":[105],"time.":[106],"phenomenon":[108],"concept":[111,122,156],"drift.":[112],"Therefore,":[113],"classification":[115,161,189],"model":[116,162,190],"drift":[123,157],"need":[125],"set":[127],"recent":[129],"instances":[130],"respective":[133],"labels":[134,150],"to":[135,137],"adapt":[136],"new":[139],"concept.":[140],"scenario,":[146],"delayed":[148],"may":[151],"not":[152,164],"timely":[154],"detection.":[158],"Thus,":[159],"capable":[166],"adapting":[168],"immediately,":[169],"implying":[170],"reduction":[172],"performance.":[174],"paper,":[177],"we":[178],"propose":[179],"framework":[181,207],"improve":[184],"through":[198],"domain":[200],"adaptation":[201],"approach":[202,225],"importance":[204],"weighting.":[205],"The":[206,220],"NIW-DSIL":[210],"(Naive":[211],"Importance":[212],"Weighting":[213],"Data":[215],"Stream":[216],"Intermediate":[218],"Latency).":[219],"experiments":[221],"showed":[222],"our":[224],"promising":[227],"dealing":[229],"scenario.":[232],"datasets,":[236],"NIW":[238],"-":[239],"DSIL":[240],"got":[241],"better":[242],"results":[243],"6":[245],"8":[247],"scenarios.":[250]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}