{"id":"https://openalex.org/W4385567612","doi":"https://doi.org/10.1145/3580305.3599409","title":"Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection","display_name":"Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385567612","doi":"https://doi.org/10.1145/3580305.3599409"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599409","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://lirias.kuleuven.be/retrieve/002db146-01c1-4c12-8bcd-4d2a9784f663","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051567564","display_name":"Lorenzo Perini","orcid":"https://orcid.org/0000-0002-5929-9727"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Lorenzo Perini","raw_affiliation_strings":["KU Leuven, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018992089","display_name":"Vincent Vercruyssen","orcid":"https://orcid.org/0000-0003-3645-3135"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Vincent Vercruyssen","raw_affiliation_strings":["KU Leuven, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029479248","display_name":"Jesse Davis","orcid":"https://orcid.org/0000-0002-3748-9263"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Jesse Davis","raw_affiliation_strings":["KU Leuven, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051567564"],"corresponding_institution_ids":["https://openalex.org/I99464096"],"apc_list":null,"apc_paid":null,"fwci":2.2468,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.90305755,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1897","last_page":"1906"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12761","display_name":"Data Stream Mining Techniques","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/anomaly-detection","display_name":"Anomaly detection","score":0.8654296398162842},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8450834155082703},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6524022817611694},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6297970414161682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6070677638053894},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5281659960746765},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.484896719455719},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4155046343803406},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.347806841135025},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3389687240123749},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06072857975959778}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8654296398162842},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8450834155082703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6524022817611694},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6297970414161682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6070677638053894},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5281659960746765},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.484896719455719},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4155046343803406},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.347806841135025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3389687240123749},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06072857975959778},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3580305.3599409","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:20.500.12942/721912","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/20.500.12942/721912","pdf_url":"https://lirias.kuleuven.be/retrieve/002db146-01c1-4c12-8bcd-4d2a9784f663","source":{"id":"https://openalex.org/S4306401954","display_name":"Lirias (KU Leuven)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I99464096","host_organization_name":"KU Leuven","host_organization_lineage":["https://openalex.org/I99464096"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, Long Beach, California, United States, 6-10 August 2023","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:lirias2repo.kuleuven.be:20.500.12942/721912","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/20.500.12942/721912","pdf_url":"https://lirias.kuleuven.be/retrieve/002db146-01c1-4c12-8bcd-4d2a9784f663","source":{"id":"https://openalex.org/S4306401954","display_name":"Lirias (KU Leuven)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I99464096","host_organization_name":"KU Leuven","host_organization_lineage":["https://openalex.org/I99464096"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, Long Beach, California, United States, 6-10 August 2023","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1638137056","display_name":null,"funder_award_id":"FWO-Vl","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G2164745409","display_name":null,"funder_award_id":"VLAIO","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G6867476589","display_name":null,"funder_award_id":"FWO-Vlaanderen","funder_id":"https://openalex.org/F4320321730","funder_display_name":"Fonds Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320313460","display_name":"Agentschap Innoveren en Ondernemen","ror":"https://ror.org/032xdry56"},{"id":"https://openalex.org/F4320321730","display_name":"Fonds Wetenschappelijk Onderzoek","ror":"https://ror.org/03qtxy027"},{"id":"https://openalex.org/F4320322308","display_name":"KU Leuven","ror":"https://ror.org/05f950310"},{"id":"https://openalex.org/F4320327336","display_name":"Vlaamse regering","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385567612.pdf"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W49716549","https://openalex.org/W62151899","https://openalex.org/W1622676895","https://openalex.org/W1916290045","https://openalex.org/W1972352301","https://openalex.org/W1996606315","https://openalex.org/W2034909454","https://openalex.org/W2057378058","https://openalex.org/W2077246985","https://openalex.org/W2117381306","https://openalex.org/W2122646361","https://openalex.org/W2123958887","https://openalex.org/W2131381395","https://openalex.org/W2133288557","https://openalex.org/W2137917285","https://openalex.org/W2296719434","https://openalex.org/W2475445698","https://openalex.org/W2558428955","https://openalex.org/W2571156573","https://openalex.org/W2767520202","https://openalex.org/W2899867782","https://openalex.org/W2908246680","https://openalex.org/W2912778443","https://openalex.org/W2963121529","https://openalex.org/W3013748601","https://openalex.org/W3030345572","https://openalex.org/W3034915883","https://openalex.org/W3130041243","https://openalex.org/W4280576978","https://openalex.org/W4283806733","https://openalex.org/W4312433903","https://openalex.org/W6839719062"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W4392946183","https://openalex.org/W3088732000","https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258"],"abstract_inverted_index":{"In":[0],"the":[1,17,22,38,49,52,80,153,176],"multi-instance":[2,145],"learning":[3,137],"(MIL)":[4],"setting":[5,146],"instances":[6,50],"are":[7,13,70,96,111],"grouped":[8],"together":[9],"into":[10],"bags.":[11],"Labels":[12],"provided":[14,112],"only":[15,93,108],"for":[16,82,127,147],"bags":[18,160,194],"and":[19,72,74,120,140,180,192,210],"not":[20],"on":[21,207],"level":[23],"of":[24,84,195],"individual":[25],"instances.":[26,85,196],"A":[27],"positive":[28,94,109,139,191],"bag":[29,39,45,53],"label":[30,46,81],"means":[31],"that":[32,156,185,212],"at":[33],"least":[34],"one":[35,75],"instance":[36],"inside":[37],"is":[40,117,132],"positive,":[41],"while":[42],"a":[43,105,144,182],"negative":[44],"restricts":[47],"all":[48],"in":[51,61,87,143,161,222],"to":[54,136,188,220],"be":[55],"negative.":[56],"MIL":[57],"data":[58,142],"naturally":[59],"arises":[60],"many":[62,88],"contexts,":[63],"such":[64],"as":[65,169],"anomaly":[66,90,148,162,172],"detection,":[67],"where":[68,107],"labels":[69,95,110],"rare":[71],"costly,":[73],"often":[76],"ends":[77],"up":[78],"annotating":[79],"sets":[83],"Moreover,":[86],"real-world":[89],"detection":[91],"problems,":[92],"collected":[97],"because":[98],"they":[99],"usually":[100],"represent":[101],"critical":[102],"events.":[103],"Such":[104],"setting,":[106],"along":[113],"with":[114],"unlabeled":[115,141,193],"data,":[116],"called":[118],"Positive":[119],"Unlabeled":[121],"(PU)":[122],"learning.":[123],"Despite":[124],"being":[125],"useful":[126],"several":[128],"use":[129],"cases,":[130],"there":[131],"no":[133],"work":[134,221],"dedicated":[135],"from":[138,158,190],"detection.":[149,163],"Therefore,":[150],"we":[151,203],"propose":[152,181],"first":[154],"method":[155,165,206],"learns":[157],"PU":[159],"Our":[164],"uses":[166],"an":[167,170],"autoencoder":[168],"underlying":[171],"detector.":[173],"We":[174,197],"alter":[175],"autoencoder's":[177],"objective":[178],"function":[179],"new":[183],"loss":[184],"allows":[186],"it":[187,213],"learn":[189],"theoretically":[198],"analyze":[199],"this":[200],"method.":[201],"Experimentally,":[202],"evaluate":[204],"our":[205,223],"30":[208],"datasets":[209],"show":[211],"performs":[214],"better":[215],"than":[216],"multiple":[217],"baselines":[218],"adapted":[219],"setting.":[224]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}