{"id":"https://openalex.org/W4306904961","doi":"https://doi.org/10.1145/3533271.3561783","title":"Guided Self-Training based Semi-Supervised Learning for Fraud Detection","display_name":"Guided Self-Training based Semi-Supervised Learning for Fraud Detection","publication_year":2022,"publication_date":"2022-10-20","ids":{"openalex":"https://openalex.org/W4306904961","doi":"https://doi.org/10.1145/3533271.3561783"},"language":"en","primary_location":{"id":"doi:10.1145/3533271.3561783","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3533271.3561783","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third ACM International Conference on AI in Finance","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/A5100306590","display_name":"Awanish Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126839","display_name":"Mastercard (United States)","ror":"https://ror.org/043cxr310","country_code":"US","type":"company","lineage":["https://openalex.org/I4210126839"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Awanish Kumar","raw_affiliation_strings":["Mastercard, IN"],"affiliations":[{"raw_affiliation_string":"Mastercard, IN","institution_ids":["https://openalex.org/I4210126839"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007069940","display_name":"Soumyadeep Ghosh","orcid":"https://orcid.org/0009-0000-6872-7976"},"institutions":[{"id":"https://openalex.org/I4210126839","display_name":"Mastercard (United States)","ror":"https://ror.org/043cxr310","country_code":"US","type":"company","lineage":["https://openalex.org/I4210126839"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Soumyadeep Ghosh","raw_affiliation_strings":["Mastercard, IN"],"affiliations":[{"raw_affiliation_string":"Mastercard, IN","institution_ids":["https://openalex.org/I4210126839"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027887984","display_name":"Janu Verma","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126839","display_name":"Mastercard (United States)","ror":"https://ror.org/043cxr310","country_code":"US","type":"company","lineage":["https://openalex.org/I4210126839"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Janu Verma","raw_affiliation_strings":["Mastercard AI Garage, IN"],"affiliations":[{"raw_affiliation_string":"Mastercard AI Garage, IN","institution_ids":["https://openalex.org/I4210126839"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100306590"],"corresponding_institution_ids":["https://openalex.org/I4210126839"],"apc_list":null,"apc_paid":null,"fwci":0.9283,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.79021494,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"148","last_page":"155"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9984999895095825,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9984999895095825,"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":0.9980999827384949,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9958999752998352,"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":0.7861931920051575},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7273021936416626},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.713259220123291},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.6557303071022034},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.6109452247619629},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5803952217102051},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5660396218299866},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5455056428909302},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.500363826751709},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49113866686820984},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.456287682056427},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42720067501068115},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.42561620473861694},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29805463552474976},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.12294930219650269}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7861931920051575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7273021936416626},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.713259220123291},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.6557303071022034},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.6109452247619629},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5803952217102051},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5660396218299866},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5455056428909302},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.500363826751709},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49113866686820984},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.456287682056427},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42720067501068115},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.42561620473861694},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29805463552474976},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.12294930219650269},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"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:10.1145/3533271.3561783","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3533271.3561783","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Third ACM International Conference on AI in Finance","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2064058256","https://openalex.org/W2136891251","https://openalex.org/W2782836818","https://openalex.org/W2895094948","https://openalex.org/W2962736666","https://openalex.org/W2988801199","https://openalex.org/W3035160371","https://openalex.org/W3068123808","https://openalex.org/W3081300507","https://openalex.org/W3102969158"],"related_works":["https://openalex.org/W4312414840","https://openalex.org/W34092691","https://openalex.org/W2794908468","https://openalex.org/W2531570999","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W4206276646","https://openalex.org/W101422005","https://openalex.org/W192740413","https://openalex.org/W2168489430"],"abstract_inverted_index":{"Semi":[0],"supervised":[1,83],"learning":[2,19,31,71,103],"has":[3,113],"attracted":[4],"attention":[5],"of":[6,17,36,57,92,120,138,168,187],"AI":[7],"researchers":[8],"in":[9,24,78,148],"the":[10,15,149,166,169,172,184,188],"recent":[11],"past,":[12],"especially":[13],"after":[14],"advent":[16],"deep":[18,30],"methods":[20],"and":[21,54,68,81,96,162],"their":[22],"success":[23],"several":[25,52],"real":[26,159],"world":[27,160],"applications.":[28],"Most":[29],"models":[32],"require":[33],"large":[34,55],"amounts":[35],"labelled":[37,64,121,193],"data,":[38],"which":[39,140],"is":[40,46,59,66,72],"expensive":[41],"to":[42,75,164],"obtain.":[43],"Fraud":[44],"detection":[45,94],"a":[47,99,110,117,125,131,136],"very":[48],"important":[49],"problem":[50],"for":[51,144],"industries":[53],"amount":[56],"data":[58,65,147,182,194],"often":[60],"available.":[61],"However,":[62],"obtaining":[63],"cumbersome":[67],"hence":[69],"semi-supervised":[70,102],"perfectly":[73],"positioned":[74],"aid":[76],"us":[77],"building":[79],"robust":[80],"accurate":[82],"models.":[84],"In":[85],"this":[86],"work,":[87],"we":[88,177],"consider":[89],"different":[90,158],"kinds":[91],"fraud":[93,175],"paradigms":[95],"show":[97,178],"that":[98,112,179],"self-training":[100,127],"based":[101],"approach":[104,128],"can":[105],"produce":[106],"significant":[107],"improvements":[108],"over":[109],"model":[111,189],"been":[114],"training":[115,150],"on":[116,156,191],"limited":[118,192],"set":[119],"data.":[122],"We":[123,152],"propose":[124],"novel":[126],"by":[129,195],"using":[130,135],"guided":[132],"sharpening":[133],"technique":[134],"pair":[137],"autoencoders":[139],"provide":[141],"useful":[142],"cues":[143],"incorporating":[145],"unlabelled":[146,181],"process.":[151],"conduct":[153],"thorough":[154],"experiments":[155],"three":[157],"databases":[161],"analysis":[163],"showcase":[165],"effectiveness":[167],"approach.":[170],"On":[171],"elliptic":[173],"bitcoin":[174],"dataset,":[176],"utilizing":[180],"improves":[183],"F1":[185],"score":[186],"trained":[190],"around":[196],"10%.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}