{"id":"https://openalex.org/W3209720970","doi":"https://doi.org/10.1145/3473910","title":"Semi-Supervised Ensemble Learning for Dealing with Inaccurate and Incomplete Supervision","display_name":"Semi-Supervised Ensemble Learning for Dealing with Inaccurate and Incomplete Supervision","publication_year":2021,"publication_date":"2021-10-22","ids":{"openalex":"https://openalex.org/W3209720970","doi":"https://doi.org/10.1145/3473910","mag":"3209720970"},"language":"en","primary_location":{"id":"doi:10.1145/3473910","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3473910","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-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/A5032311062","display_name":"Mona Nashaat","orcid":"https://orcid.org/0000-0002-7580-5757"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mona Nashaat","raw_affiliation_strings":["Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022645420","display_name":"Aindrila Ghosh","orcid":"https://orcid.org/0000-0003-4908-9491"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Aindrila Ghosh","raw_affiliation_strings":["Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046704046","display_name":"James A. Miller","orcid":"https://orcid.org/0000-0002-4023-4296"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"James Miller","raw_affiliation_strings":["Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031825849","display_name":"Shaikh Quader","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113654","display_name":"IBM (Canada)","ror":"https://ror.org/025sxka56","country_code":"CA","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210113654"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Shaikh Quader","raw_affiliation_strings":["IBM Canada Software Lab, IBM Canada, Toronto, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Canada Software Lab, IBM Canada, Toronto, Ontario, Canada","institution_ids":["https://openalex.org/I4210113654"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8396,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.79286233,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"16","issue":"3","first_page":"1","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9894999861717224,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.783703088760376},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6936725974082947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6506986021995544},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6312769055366516},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6109150648117065},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5447136163711548},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4919416606426239},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.45833900570869446},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.43604761362075806},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37080439925193787},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07663840055465698}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.783703088760376},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6936725974082947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6506986021995544},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6312769055366516},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6109150648117065},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5447136163711548},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4919416606426239},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.45833900570869446},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.43604761362075806},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37080439925193787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07663840055465698},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3473910","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3473910","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W22120349","https://openalex.org/W1565746575","https://openalex.org/W1667009198","https://openalex.org/W1986242337","https://openalex.org/W2008989859","https://openalex.org/W2009514435","https://openalex.org/W2021732807","https://openalex.org/W2167460663","https://openalex.org/W2187303492","https://openalex.org/W2408920689","https://openalex.org/W2413832562","https://openalex.org/W2469003029","https://openalex.org/W2470642288","https://openalex.org/W2523754241","https://openalex.org/W2590150025","https://openalex.org/W2616306692","https://openalex.org/W2738442292","https://openalex.org/W2746791238","https://openalex.org/W2767094803","https://openalex.org/W2767543837","https://openalex.org/W2789677693","https://openalex.org/W2793169573","https://openalex.org/W2795974822","https://openalex.org/W2809611810","https://openalex.org/W2815209373","https://openalex.org/W2907974448","https://openalex.org/W2913176359","https://openalex.org/W2913668833","https://openalex.org/W2937746382","https://openalex.org/W2949413565","https://openalex.org/W2951094201","https://openalex.org/W2964181345","https://openalex.org/W3008257486","https://openalex.org/W3196939611","https://openalex.org/W4206073500","https://openalex.org/W4238521532","https://openalex.org/W4312092243","https://openalex.org/W6806144174"],"related_works":["https://openalex.org/W4312414840","https://openalex.org/W34092691","https://openalex.org/W2794908468","https://openalex.org/W2531570999","https://openalex.org/W4206276646","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W101422005","https://openalex.org/W192740413","https://openalex.org/W3004135598"],"abstract_inverted_index":{"In":[0],"real-world":[1],"tasks,":[2],"obtaining":[3],"a":[4,66,88,110],"large":[5],"set":[6],"of":[7,41,51,91,136,148,168,181],"noise-free":[8],"data":[9,98],"can":[10],"be":[11,141],"prohibitively":[12],"expensive.":[13],"Therefore,":[14,58],"recent":[15],"research":[16],"tries":[17],"to":[18,22,55,76,117,132,144],"enable":[19],"machine":[20],"learning":[21,93],"work":[23],"with":[24,78,113],"weakly":[25],"supervised":[26,82],"datasets,":[27],"such":[28],"as":[29],"inaccurate":[30,79],"or":[31],"incomplete":[32,81],"data.":[33,106],"However,":[34],"the":[35,96,104,120,124,134,146,149,154,159,165,169,179,182],"previous":[36],"literature":[37],"treats":[38],"each":[39],"type":[40],"weak":[42,52],"supervision":[43,53],"individually,":[44],"although,":[45],"in":[46,59,94,185],"most":[47],"cases,":[48],"different":[49],"types":[50],"tend":[54],"occur":[56],"simultaneously.":[57],"this":[60],"article,":[61],"we":[62,157],"present":[63],"Smart":[64],"MEnDR,":[65],"Classification":[67],"Model":[68],"that":[69],"applies":[70,87,127],"Ensemble":[71],"Learning":[72],"and":[73,80,164,191],"Data-driven":[74],"Rectification":[75],"deal":[77],"datasets.":[83],"The":[84,107,175],"model":[85],"first":[86],"preliminary":[89],"phase":[90,108],"ensemble":[92],"which":[95,138],"noisy":[97],"points":[99,139],"are":[100],"detected":[101],"while":[102],"exploiting":[103],"unlabelled":[105],"employs":[109],"semi-supervised":[111],"technique":[112],"maximum":[114],"likelihood":[115],"estimation":[116],"decide":[118],"on":[119],"disagreement":[121],"rate.":[122],"Second,":[123],"proposed":[125,155,170,183],"approach":[126],"an":[128],"iterative":[129],"meta-learning":[130],"step":[131],"tackle":[133],"problem":[135],"knowing":[137],"should":[140],"made":[142],"correct":[143,189],"improve":[145],"performance":[147],"final":[150],"classifier.":[151],"To":[152],"evaluate":[153],"framework,":[156],"report":[158],"classification":[160,194],"performance,":[161],"noise":[162],"detection,":[163],"labelling":[166],"accuracy":[167],"method":[171],"against":[172],"state-of-the-art":[173],"techniques.":[174],"experimental":[176],"results":[177],"demonstrate":[178],"effectiveness":[180],"framework":[184],"detecting":[186],"noise,":[187],"providing":[188],"labels,":[190],"attaining":[192],"high":[193],"performance.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
