{"id":"https://openalex.org/W3014537909","doi":"https://doi.org/10.1145/3341105.3373890","title":"A generative semi-supervised classifier for datasets with unknown classes","display_name":"A generative semi-supervised classifier for datasets with unknown classes","publication_year":2020,"publication_date":"2020-03-29","ids":{"openalex":"https://openalex.org/W3014537909","doi":"https://doi.org/10.1145/3341105.3373890","mag":"3014537909"},"language":"en","primary_location":{"id":"doi:10.1145/3341105.3373890","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341105.3373890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 35th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://zenodo.org/record/3736217","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067345261","display_name":"Stefan Schrunner","orcid":"https://orcid.org/0000-0003-1327-4855"},"institutions":[{"id":"https://openalex.org/I1281400175","display_name":"Oslo University Hospital","ror":"https://ror.org/00j9c2840","country_code":"NO","type":"healthcare","lineage":["https://openalex.org/I1281400175"]}],"countries":["NO"],"is_corresponding":true,"raw_author_name":"Stefan Schrunner","raw_affiliation_strings":["Oslo University Hospital, Oslo, Norway"],"affiliations":[{"raw_affiliation_string":"Oslo University Hospital, Oslo, Norway","institution_ids":["https://openalex.org/I1281400175"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003584509","display_name":"Bernhard C. Geiger","orcid":"https://orcid.org/0000-0003-3257-743X"},"institutions":[{"id":"https://openalex.org/I4210088621","display_name":"Know Center Research GmbH (Austria)","ror":"https://ror.org/004zhad81","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210088621"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Bernhard C. Geiger","raw_affiliation_strings":["Know-Center GmbH, Graz, Austria"],"affiliations":[{"raw_affiliation_string":"Know-Center GmbH, Graz, Austria","institution_ids":["https://openalex.org/I4210088621"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029164463","display_name":"Anja Zernig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anja Zernig","raw_affiliation_strings":["KAI GmbH, Villach, Austria"],"affiliations":[{"raw_affiliation_string":"KAI GmbH, Villach, Austria","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014398832","display_name":"Roman Kern","orcid":"https://orcid.org/0000-0003-0202-6100"},"institutions":[{"id":"https://openalex.org/I4210088621","display_name":"Know Center Research GmbH (Austria)","ror":"https://ror.org/004zhad81","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210088621"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Roman Kern","raw_affiliation_strings":["Know-Center GmbH, Graz, Austria"],"affiliations":[{"raw_affiliation_string":"Know-Center GmbH, Graz, Austria","institution_ids":["https://openalex.org/I4210088621"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067345261"],"corresponding_institution_ids":["https://openalex.org/I1281400175"],"apc_list":null,"apc_paid":null,"fwci":0.6797,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.76003652,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1066","last_page":"1074"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9986000061035156,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9986000061035156,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9965999722480774,"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":0.9962999820709229,"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":0.7609359622001648},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.69907546043396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6962348818778992},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6446361541748047},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.590178906917572},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.576683759689331},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5616013407707214},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.547476589679718},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5128786563873291},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5003278255462646},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4849950969219208},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.45568206906318665},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38930457830429077},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34557509422302246},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12403106689453125}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609359622001648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.69907546043396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6962348818778992},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6446361541748047},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.590178906917572},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.576683759689331},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5616013407707214},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.547476589679718},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5128786563873291},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5003278255462646},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4849950969219208},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.45568206906318665},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38930457830429077},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34557509422302246},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12403106689453125},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3341105.3373890","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341105.3373890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 35th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:zenodo.org:3736217","is_oa":true,"landing_page_url":"https://zenodo.org/record/3736217","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:3736217","is_oa":true,"landing_page_url":"https://zenodo.org/record/3736217","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2750739462","display_name":null,"funder_award_id":"692466","funder_id":"https://openalex.org/F4320327207","funder_display_name":"Electronic Components and Systems for European Leadership"},{"id":"https://openalex.org/G3437061614","display_name":null,"funder_award_id":"ECSEL JU","funder_id":"https://openalex.org/F4320327207","funder_display_name":"Electronic Components and Systems for European Leadership"}],"funders":[{"id":"https://openalex.org/F4320323033","display_name":"Bundesministerium f\u00fcr Verkehr, Innovation und Technologie","ror":"https://ror.org/04marky29"},{"id":"https://openalex.org/F4320327207","display_name":"Electronic Components and Systems for European Leadership","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W22461475","https://openalex.org/W55606816","https://openalex.org/W1861993554","https://openalex.org/W2018459374","https://openalex.org/W2065708566","https://openalex.org/W2097720545","https://openalex.org/W2105497548","https://openalex.org/W2112796928","https://openalex.org/W2114315281","https://openalex.org/W2119880843","https://openalex.org/W2148687775","https://openalex.org/W2561675875","https://openalex.org/W2595788441","https://openalex.org/W2595840341","https://openalex.org/W2611453196","https://openalex.org/W2772976938","https://openalex.org/W2942524263","https://openalex.org/W3211093240"],"related_works":["https://openalex.org/W34092691","https://openalex.org/W4312414840","https://openalex.org/W2794908468","https://openalex.org/W4206276646","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W101422005","https://openalex.org/W192740413","https://openalex.org/W4300902524","https://openalex.org/W2168489430"],"abstract_inverted_index":{"Classification":[0],"has":[1,19],"been":[2,20],"tackled":[3],"by":[4],"a":[5,12,27,74,83,99,106,118,127,149,165,184],"large":[6],"number":[7],"of":[8,36,57,65,76,90,121,130,167,191],"algorithms,":[9],"predominantly":[10],"following":[11],"supervised":[13],"learning":[14,178],"setting.":[15],"Surprisingly":[16],"little":[17],"research":[18],"devoted":[21],"to":[22,86],"the":[23,55,63,66,77,88,136,155,189,192],"problem":[24,89],"setting":[25],"where":[26,54,72,154],"dataset":[28,138],"is":[29,59,68,79,159,195],"only":[30,73],"partially":[31],"labeled,":[32],"including":[33,70],"even":[34],"instances":[35],"entirely":[37],"unlabeled":[38,131],"classes.":[39,146],"Algorithmic":[40],"solutions":[41],"that":[42,135,169],"are":[43,48],"suited":[44,197],"for":[45,198],"such":[46],"problems":[47],"especially":[49],"important":[50],"in":[51,124,164],"practical":[52,151,199],"scenarios,":[53],"labelling":[56],"data":[58,67,123],"prohibitively":[60],"expensive,":[61],"or":[62],"understanding":[64,190],"lacking,":[69],"cases,":[71],"subset":[75],"classes":[78],"known.":[80],"We":[81,162],"present":[82,145],"generative":[84,185],"method":[85],"address":[87],"semi-supervised":[91,177],"classification":[92],"with":[93,126],"unknown":[94],"classes,":[95],"whereby":[96],"we":[97,104],"follow":[98],"Bayesian":[100,111],"perspective.":[101],"In":[102],"detail,":[103],"apply":[105],"two-step":[107],"procedure":[108],"based":[109],"on":[110],"classifiers":[112],"and":[113],"exploit":[114],"information":[115],"from":[116,143],"both":[117],"small":[119],"set":[120,129,158],"labeled":[122,137,156],"combination":[125],"larger":[128],"training":[132,157],"data,":[133,193],"allowing":[134],"does":[139],"not":[140,160],"contain":[141],"samples":[142],"all":[144],"This":[147],"represents":[148],"common":[150],"application":[152],"setup,":[153],"exhaustive.":[161],"show":[163],"series":[166],"experiments":[168],"our":[170,181],"approach":[171,182],"outperforms":[172],"state-of-the-art":[173],"methods":[174],"tackling":[175],"similar":[176],"problems.":[179],"Since":[180],"yields":[183],"model,":[186],"which":[187],"aids":[188],"it":[194],"particularly":[196],"applications.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2020-04-10T00:00:00"}