{"id":"https://openalex.org/W2999684949","doi":"https://doi.org/10.1109/icmlc48188.2019.8949214","title":"Multi-Label Active Learning Driven by Uncertainty and Inconsistency","display_name":"Multi-Label Active Learning Driven by Uncertainty and Inconsistency","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2999684949","doi":"https://doi.org/10.1109/icmlc48188.2019.8949214","mag":"2999684949"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc48188.2019.8949214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc48188.2019.8949214","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5114377922","display_name":"Ran Wang","orcid":"https://orcid.org/0000-0002-2586-5604"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Wang","raw_affiliation_strings":["Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University,Shenzhen,China,518060","Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University,Shenzhen,China,518060","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026490578","display_name":"Suhe Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suhe Ye","raw_affiliation_strings":["Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University,Shenzhen,China,518060","Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University,Shenzhen,China,518060","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":0.2187,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55320293,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9979000091552734,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9979000091552734,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9800000190734863,"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/computer-science","display_name":"Computer science","score":0.6902133226394653},{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.6872391700744629},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6559399366378784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5722552537918091},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5668703317642212},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.45025479793548584},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4302127957344055},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3725765347480774}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6902133226394653},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.6872391700744629},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6559399366378784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5722552537918091},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5668703317642212},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.45025479793548584},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4302127957344055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3725765347480774}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc48188.2019.8949214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc48188.2019.8949214","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W167321734","https://openalex.org/W1504412414","https://openalex.org/W1664950380","https://openalex.org/W1964017540","https://openalex.org/W1967145187","https://openalex.org/W1993898444","https://openalex.org/W2018901336","https://openalex.org/W2114315281","https://openalex.org/W2139821863","https://openalex.org/W2142550050","https://openalex.org/W2156935079","https://openalex.org/W2278864798","https://openalex.org/W2401282582","https://openalex.org/W2648561407","https://openalex.org/W4250800088","https://openalex.org/W6606838427","https://openalex.org/W6637241018","https://openalex.org/W6680614269","https://openalex.org/W6713235370"],"related_works":["https://openalex.org/W4319452359","https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W2169074127","https://openalex.org/W83146503","https://openalex.org/W2163707935","https://openalex.org/W202723009","https://openalex.org/W2188612292","https://openalex.org/W4206462905","https://openalex.org/W2165396616"],"abstract_inverted_index":{"Multi-label":[0],"active":[1],"learning":[2,41],"(MLAL)":[3],"can":[4,46,61],"help":[5],"learn":[6],"a":[7,13,27,33,101,109],"highperformance":[8],"multi-label":[9],"classifier":[10,45],"based":[11,68],"on":[12,69,105],"smaller":[14],"training":[15],"set":[16,76],"by":[17,31],"selecting":[18],"and":[19,120],"labeling":[20],"high-quality":[21],"samples":[22],"iteratively.":[23],"This":[24],"paper":[25],"proposes":[26],"new":[28,34,110],"MLAL":[29,111],"algorithm":[30],"introducing":[32],"concept":[35],"called":[36],"inconsistency":[37,79],"cardinality.":[38],"During":[39],"each":[40,59],"iteration,":[42],"the":[43,52,65,70,84,118,123],"current":[44],"produce":[47],"temporal":[48],"decision":[49,57],"labels":[50],"for":[51],"candidate":[53],"samples.":[54,107],"Then,":[55],"supporting":[56],"regarding":[58],"label":[60],"be":[62,95],"derived":[63],"from":[64],"conditional":[66],"features":[67],"lower":[71],"approximations":[72],"in":[73],"fuzzy":[74],"rough":[75],"theory.":[77],"The":[78],"cardinality":[80],"is":[81,113],"defined":[82],"as":[83],"disagreement":[85],"degree":[86],"between":[87],"these":[88],"two":[89],"sets":[90],"of":[91,122],"decisions,":[92],"which":[93],"will":[94],"combined":[96],"with":[97],"uncertainty":[98],"to":[99],"give":[100],"more":[102],"comprehensive":[103],"evaluation":[104],"unlabeled":[106],"Correspondingly,":[108],"strategy":[112],"proposed.":[114],"Experimental":[115],"comparisons":[116],"show":[117],"feasibility":[119],"effectiveness":[121],"proposed":[124],"algorithm.":[125]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
