{"id":"https://openalex.org/W2207622907","doi":"https://doi.org/10.1109/bigdata.2015.7363766","title":"Hybrid active learning for non-stationary streaming data with asynchronous labeling","display_name":"Hybrid active learning for non-stationary streaming data with asynchronous labeling","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2207622907","doi":"https://doi.org/10.1109/bigdata.2015.7363766","mag":"2207622907"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7363766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","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/A5100753445","display_name":"Hyunjoo Kim","orcid":"https://orcid.org/0000-0002-6716-7740"},"institutions":[{"id":"https://openalex.org/I173498003","display_name":"Palo Alto Research Center","ror":"https://ror.org/0529fxt39","country_code":"US","type":"facility","lineage":["https://openalex.org/I173498003","https://openalex.org/I4210132870"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hyunjoo Kim","raw_affiliation_strings":["Palo Alto Research Center, Webster, New York, USA"],"affiliations":[{"raw_affiliation_string":"Palo Alto Research Center, Webster, New York, USA","institution_ids":["https://openalex.org/I173498003"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000159830","display_name":"Sriganesh Madhvanath","orcid":null},"institutions":[{"id":"https://openalex.org/I173498003","display_name":"Palo Alto Research Center","ror":"https://ror.org/0529fxt39","country_code":"US","type":"facility","lineage":["https://openalex.org/I173498003","https://openalex.org/I4210132870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sriganesh Madhvanath","raw_affiliation_strings":["Palo Alto Research Center, Webster, New York, USA"],"affiliations":[{"raw_affiliation_string":"Palo Alto Research Center, Webster, New York, USA","institution_ids":["https://openalex.org/I173498003"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101795165","display_name":"Tong Sun","orcid":"https://orcid.org/0000-0001-7298-0333"},"institutions":[{"id":"https://openalex.org/I173498003","display_name":"Palo Alto Research Center","ror":"https://ror.org/0529fxt39","country_code":"US","type":"facility","lineage":["https://openalex.org/I173498003","https://openalex.org/I4210132870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tong Sun","raw_affiliation_strings":["Palo Alto Research Center, Webster, New York, USA"],"affiliations":[{"raw_affiliation_string":"Palo Alto Research Center, Webster, New York, USA","institution_ids":["https://openalex.org/I173498003"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100753445"],"corresponding_institution_ids":["https://openalex.org/I173498003"],"apc_list":null,"apc_paid":null,"fwci":1.7258,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.88632707,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"287","last_page":"292"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.988099992275238,"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":0.8401881456375122},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.802880048751831},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.7438232898712158},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6789515018463135},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.6788552403450012},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.673258364200592},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5502526760101318},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5463334918022156},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.5187894701957703},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.481479287147522},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4574323296546936},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.4516526758670807},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3480857312679291}],"concepts":[{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.8401881456375122},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.802880048751831},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.7438232898712158},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6789515018463135},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.6788552403450012},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.673258364200592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5502526760101318},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5463334918022156},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.5187894701957703},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.481479287147522},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4574323296546936},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.4516526758670807},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3480857312679291},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7363766","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363766","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W34738725","https://openalex.org/W250005488","https://openalex.org/W1515163912","https://openalex.org/W1529614826","https://openalex.org/W1585854823","https://openalex.org/W1635892993","https://openalex.org/W1765145123","https://openalex.org/W1819097978","https://openalex.org/W1926038792","https://openalex.org/W1981600205","https://openalex.org/W1982039810","https://openalex.org/W2009109028","https://openalex.org/W2026261766","https://openalex.org/W2037242621","https://openalex.org/W2052300809","https://openalex.org/W2108740451","https://openalex.org/W2139327121","https://openalex.org/W2160512933","https://openalex.org/W2161391345","https://openalex.org/W2168386304","https://openalex.org/W2203118094","https://openalex.org/W2279802117","https://openalex.org/W2283440024","https://openalex.org/W2903158431","https://openalex.org/W3098372638","https://openalex.org/W6756615331"],"related_works":["https://openalex.org/W2981673118","https://openalex.org/W2759864402","https://openalex.org/W2601363847","https://openalex.org/W3093884210","https://openalex.org/W2060628068","https://openalex.org/W2940903377","https://openalex.org/W2761091637","https://openalex.org/W3006639312","https://openalex.org/W3000948009","https://openalex.org/W1560789270"],"abstract_inverted_index":{"Active":[0,19,109],"learning":[1,96],"enables":[2],"supervised":[3],"classifiers":[4],"to":[5,33,68,78,117,148,165],"learn":[6],"using":[7],"fewer":[8],"labeled":[9,35,70],"samples,":[10],"by":[11,43],"actively":[12],"selecting":[13],"samples":[14],"for":[15,107,152],"human":[16],"labeling.":[17],"Most":[18],"Learning":[20,110],"approaches":[21],"can":[22],"be":[23,34,69,133],"categorized":[24],"as":[25,85],"pool-based":[26,91,113,156,173],"or":[27,71],"stream-based.":[28],"Pool-based":[29],"strategies":[30,49,74,92,116],"select":[31],"instances":[32,151],"from":[36,154],"the":[37,54,76,80,86,119,155,168,172,177],"available":[38],"pool":[39],"of":[40,57,121,146,171,179],"unlabeled":[41,58],"data,":[42],"evaluating":[44],"each":[45],"instance,":[46],"whereas":[47],"stream-based":[48,115,158],"examine":[50],"every":[51],"instance":[52,67],"in":[53,123],"incoming":[55,87],"stream":[56],"data":[59,88,126],"and":[60,105,114,135,157,160],"decide":[61],"sequentially":[62],"whether":[63],"they":[64],"want":[65],"that":[66,111],"not.":[72],"Stream-based":[73],"enable":[75],"ability":[77],"adapt":[79],"classifier":[81],"model":[82],"more":[83],"quickly":[84],"changes,":[89],"while":[90],"often":[93],"exhibit":[94],"better":[95],"rates.":[97],"In":[98,139],"this":[99],"paper,":[100],"we":[101,141],"propose":[102,142],"a":[103,124],"framework":[104],"method":[106],"Hybrid":[108],"integrates":[112],"harvest":[118],"benefits":[120],"both,":[122],"streaming":[125],"classification":[127],"scenario":[128],"where":[129],"concept":[130,180],"drift":[131],"may":[132],"prevalent,":[134],"labeling":[136,153],"is":[137],"asynchronous.":[138],"addition,":[140],"(i)":[143],"prioritized":[144],"aggregation":[145],"selection":[147],"combine":[149],"selected":[150],"strategies,":[159],"(ii)":[161],"batch":[162],"period":[163],"adaptation":[164],"dynamically":[166],"change":[167],"triggering":[169],"pattern":[170],"strategy":[174],"based":[175],"upon":[176],"detection":[178],"drift.":[181]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}