{"id":"https://openalex.org/W3166297290","doi":"https://doi.org/10.1145/3447548.3467259","title":"Partial Multi-Label Learning with Meta Disambiguation","display_name":"Partial Multi-Label Learning with Meta Disambiguation","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3166297290","doi":"https://doi.org/10.1145/3447548.3467259","mag":"3166297290"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467259","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","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/A5085720255","display_name":"Ming-Kun Xie","orcid":"https://orcid.org/0000-0002-1053-1409"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ming-Kun Xie","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103139435","display_name":"Feng Sun","orcid":"https://orcid.org/0000-0002-8012-2993"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Sun","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103204774","display_name":"Sheng-Jun Huang","orcid":"https://orcid.org/0000-0002-7673-5367"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng-Jun Huang","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5085720255"],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":2.6553,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.91465409,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1904","last_page":"1912"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9768000245094299,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9609000086784363,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.781537652015686},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6776486039161682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6601126790046692},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6466345191001892},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6105192303657532},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5149644017219543},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4912232756614685},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45900508761405945},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.4509279131889343},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3698423504829407},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.346704363822937}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.781537652015686},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6776486039161682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6601126790046692},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6466345191001892},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6105192303657532},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5149644017219543},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4912232756614685},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45900508761405945},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.4509279131889343},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3698423504829407},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.346704363822937},{"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:10.1145/3447548.3467259","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining","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":28,"referenced_works":["https://openalex.org/W66588809","https://openalex.org/W1565746575","https://openalex.org/W2066340877","https://openalex.org/W2114315281","https://openalex.org/W2118712128","https://openalex.org/W2733555913","https://openalex.org/W2753160622","https://openalex.org/W2807945348","https://openalex.org/W2808448059","https://openalex.org/W2903641274","https://openalex.org/W2904398352","https://openalex.org/W2905563251","https://openalex.org/W2905654560","https://openalex.org/W2907230706","https://openalex.org/W2912269676","https://openalex.org/W2932399282","https://openalex.org/W2963943197","https://openalex.org/W2964782060","https://openalex.org/W2997124596","https://openalex.org/W2997519153","https://openalex.org/W2998605053","https://openalex.org/W3015362220","https://openalex.org/W3015464002","https://openalex.org/W3080408769","https://openalex.org/W3103626843","https://openalex.org/W3127280529","https://openalex.org/W3133128010","https://openalex.org/W6677758222"],"related_works":["https://openalex.org/W4401571341","https://openalex.org/W2044488462","https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W2169074127","https://openalex.org/W2163707935","https://openalex.org/W83146503","https://openalex.org/W202723009","https://openalex.org/W4206462905","https://openalex.org/W2165396616"],"abstract_inverted_index":{"In":[0,69],"partial":[1,40,78],"multi-label":[2,79,108],"learning":[3,80],"(PML)":[4],"problems,":[5,29],"each":[6,136],"instance":[7],"is":[8,110,139],"partially":[9],"annotated":[10],"with":[11,81,142,161],"a":[12,74,101,114,146],"candidate":[13,137],"label":[14,127,138],"set,":[15],"which":[16,118],"consists":[17],"of":[18,86,178],"multiple":[19,169],"relevant":[20],"labels":[21,99],"and":[22,97,171],"some":[23],"noisy":[24,98],"labels.":[25],"To":[26,150],"solve":[27],"PML":[28,67],"existing":[30],"methods":[31],"typically":[32],"try":[33,92],"to":[34,65,93,125],"recover":[35],"the":[36,47,51,58,107,121,126,130,133,153,176,179],"ground-truth":[37,96],"information":[38,123],"from":[39],"annotations":[41],"based":[42],"on":[43,46,88,129,145,168],"extra":[44,89],"assumptions":[45,52],"data":[48],"structures.":[49],"While":[50],"hardly":[53],"hold":[54],"in":[55,100],"real-world":[56],"applications,":[57],"trained":[59,111],"model":[60],"may":[61],"not":[62],"generalize":[63],"well":[64],"varied":[66,172],"tasks.":[68],"this":[70],"paper,":[71],"we":[72,91],"propose":[73],"novel":[75],"approach":[76],"for":[77,135],"meta":[82],"disambiguation":[83],"(PML-MD).":[84],"Instead":[85],"relying":[87],"assumptions,":[90],"disambiguate":[94],"between":[95],"meta-learning":[102],"fashion.":[103],"On":[104],"one":[105],"hand,":[106,132],"classifier":[109],"by":[112],"minimizing":[113],"confidence-weighted":[115],"ranking":[116],"loss,":[117],"distinctively":[119],"utilizes":[120],"supervised":[122],"according":[124],"quality;":[128],"other":[131],"confidence":[134],"adaptively":[140],"estimated":[141],"its":[143],"performance":[144],"small":[147],"validation":[148],"set.":[149],"speed":[151],"up":[152],"optimization,":[154],"these":[155],"two":[156],"procedures":[157],"are":[158],"performed":[159],"alternately":[160],"an":[162],"online":[163],"approximation":[164],"strategy.":[165],"Comprehensive":[166],"experiments":[167],"datasets":[170],"evaluation":[173],"metrics":[174],"validate":[175],"effectiveness":[177],"proposed":[180],"method.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}
