{"id":"https://openalex.org/W4403212800","doi":"https://doi.org/10.1109/icecet61485.2024.10698625","title":"Reinforcement Learning for Optimizing Conditional Tabular Generative Adversarial Networks Hyperparameters in Financial Fraud Detection","display_name":"Reinforcement Learning for Optimizing Conditional Tabular Generative Adversarial Networks Hyperparameters in Financial Fraud Detection","publication_year":2024,"publication_date":"2024-07-25","ids":{"openalex":"https://openalex.org/W4403212800","doi":"https://doi.org/10.1109/icecet61485.2024.10698625"},"language":"en","primary_location":{"id":"doi:10.1109/icecet61485.2024.10698625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet61485.2024.10698625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Electrical, Computer and Energy Technologies (ICECET","raw_type":"proceedings-article"},"type":"conference-paper","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/A5092760485","display_name":"Patience Chew Yee Cheah","orcid":"https://orcid.org/0000-0001-8067-423X"},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Patience Chew Yee Cheah","raw_affiliation_strings":["School of Computer Science, University of Nottingham Ningbo China,Ningbo,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Nottingham Ningbo China,Ningbo,China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058777008","display_name":"Yue Yang","orcid":"https://orcid.org/0000-0001-5670-9551"},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Yang","raw_affiliation_strings":["School of Computer Science, University of Nottingham Ningbo China,Ningbo,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Nottingham Ningbo China,Ningbo,China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053615344","display_name":"Boon Giin Lee","orcid":"https://orcid.org/0000-0001-5743-1010"},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Boon Giin Lee","raw_affiliation_strings":["School of Computer Science, University of Nottingham Ningbo China,Ningbo,China"],"raw_orcid":"https://orcid.org/0000-0001-5743-1010","affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Nottingham Ningbo China,Ningbo,China","institution_ids":["https://openalex.org/I13591777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I13591777"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17954807,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9847999811172485,"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":0.9847999811172485,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9442999958992004,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9021000266075134,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/adversarial-system","display_name":"Adversarial system","score":0.8503478765487671},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.8487088680267334},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.761198878288269},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700434148311615},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6365693211555481},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.562811553478241},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.543533205986023},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.47614264488220215},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.46187591552734375},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1849827766418457},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1418060064315796}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8503478765487671},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8487088680267334},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.761198878288269},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700434148311615},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6365693211555481},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.562811553478241},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.543533205986023},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.47614264488220215},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.46187591552734375},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1849827766418457},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1418060064315796},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icecet61485.2024.10698625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet61485.2024.10698625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Electrical, Computer and Energy Technologies (ICECET","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G8395456720","display_name":null,"funder_award_id":"2021B-008-C","funder_id":"https://openalex.org/F4320324778","funder_display_name":"Ningbo Municipal Bureau of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320324778","display_name":"Ningbo Municipal Bureau of Science and Technology","ror":"https://ror.org/00gskyj95"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2101234009","https://openalex.org/W2123859855","https://openalex.org/W2148143831","https://openalex.org/W2192203593","https://openalex.org/W2295598076","https://openalex.org/W2338318698","https://openalex.org/W2746553466","https://openalex.org/W2779931100","https://openalex.org/W2800788706","https://openalex.org/W3035825996","https://openalex.org/W3037958117","https://openalex.org/W3124330389","https://openalex.org/W3186409582","https://openalex.org/W3190520168","https://openalex.org/W3209247505","https://openalex.org/W3216660278","https://openalex.org/W4386461659","https://openalex.org/W6637568146","https://openalex.org/W6637967152","https://openalex.org/W6674385629","https://openalex.org/W6676179485","https://openalex.org/W6685444567","https://openalex.org/W6745609711","https://openalex.org/W6746090280","https://openalex.org/W6765121640","https://openalex.org/W6765451912","https://openalex.org/W6805163760","https://openalex.org/W6907693833"],"related_works":["https://openalex.org/W4281847915","https://openalex.org/W3000635674","https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W4385421777","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2845413374","https://openalex.org/W3005996785","https://openalex.org/W4297411772"],"abstract_inverted_index":{"Financial":[0],"fraud":[1],"datasets":[2],"typically":[3],"exhibit":[4],"a":[5,41],"pro-nounced":[6],"class":[7],"imbalance,":[8],"leading":[9],"to":[10,53,90,100],"elevated":[11],"false":[12],"positive":[13],"rates":[14],"when":[15],"employing":[16],"machine":[17],"learning":[18,75],"classifiers.":[19],"To":[20],"mitigate":[21],"this":[22,80,117],"issue,":[23],"the":[24,57,70,84,92,107,113,124,131,136,144],"Conditional":[25],"Tabular":[26],"Generative":[27],"Adversarial":[28],"Network":[29],"(CTGAN),":[30],"an":[31,102],"oversampling":[32],"technique":[33],"tailored":[34],"for":[35,77,126],"tabular":[36],"data,":[37],"has":[38],"emerged":[39],"as":[40],"promising":[42],"solution":[43],"by":[44],"generating":[45],"additional":[46],"minority":[47],"samples.":[48],"However,":[49],"it":[50],"remains":[51],"pertinent":[52],"investigate":[54],"whether":[55],"optimizing":[56],"hyperparameters":[58,93,149],"governing":[59],"CTGAN":[60,110,139,148],"can":[61],"yield":[62],"further":[63],"improvements":[64],"in":[65,135],"performance.":[66,152],"In":[67],"light":[68],"of":[69,73,86,94,109,133,138,147],"recent":[71],"application":[72],"reinforcement":[74],"(RL)":[76],"hyperparameter":[78,140],"tuning,":[79],"study":[81],"delves":[82],"into":[83],"potential":[85,132],"harnessing":[87],"RL":[88,134],"mod-els":[89],"fine-tune":[91],"CTGAN.":[95],"The":[96],"overarching":[97],"objective":[98],"is":[99],"design":[101],"RL-based":[103],"methodology":[104],"that":[105],"automates":[106],"optimization":[108],"hyperparameters.":[111],"Although":[112],"initial":[114],"findings":[115],"from":[116],"endeavor":[118],"may":[119],"be":[120],"inconclusive,":[121],"they":[122],"pave":[123],"way":[125],"future":[127],"research":[128],"avenues,":[129],"exploring":[130],"context":[137],"tuning":[141],"and":[142],"scrutinizing":[143],"nuanced":[145],"effects":[146],"on":[150],"classification":[151]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}