{"id":"https://openalex.org/W4388505581","doi":"https://doi.org/10.1109/tai.2023.3330949","title":"Synthetic Information Toward Maximum Posterior Ratio for Deep Learning on Imbalanced Data","display_name":"Synthetic Information Toward Maximum Posterior Ratio for Deep Learning on Imbalanced Data","publication_year":2023,"publication_date":"2023-11-08","ids":{"openalex":"https://openalex.org/W4388505581","doi":"https://doi.org/10.1109/tai.2023.3330949"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2023.3330949","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2023.3330949","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.02591","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106406947","display_name":"Hung T. Nguyen","orcid":"https://orcid.org/0000-0001-5474-8127"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hung Nguyen","raw_affiliation_strings":["Department of Electrical Engineering, University of South Florida, Tampa, FL, USA"],"raw_orcid":"https://orcid.org/0000-0001-5474-8127","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055158787","display_name":"J. Morris Chang","orcid":"https://orcid.org/0000-0002-0660-7191"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Morris Chang","raw_affiliation_strings":["Department of Electrical Engineering, University of South Florida, Tampa, FL, USA"],"raw_orcid":"https://orcid.org/0000-0002-0660-7191","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5106406947"],"corresponding_institution_ids":["https://openalex.org/I2613432"],"apc_list":null,"apc_paid":null,"fwci":0.5112,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72387334,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"5","issue":"6","first_page":"2790","last_page":"2804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","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/T11652","display_name":"Imbalanced Data Classification Techniques","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/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9922000169754028,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.4988064765930176},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.38290709257125854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.320059597492218}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4988064765930176},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38290709257125854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.320059597492218}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tai.2023.3330949","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2023.3330949","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2401.02591","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.02591","pdf_url":"https://arxiv.org/pdf/2401.02591","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.02591","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.02591","pdf_url":"https://arxiv.org/pdf/2401.02591","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G424247011","display_name":null,"funder_award_id":"H92222-15-3-0001-01","funder_id":"https://openalex.org/F4320334118","funder_display_name":"United States Special Operations Command"}],"funders":[{"id":"https://openalex.org/F4320334118","display_name":"United States Special Operations Command","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388505581.pdf","grobid_xml":"https://content.openalex.org/works/W4388505581.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W1513123914","https://openalex.org/W1563938718","https://openalex.org/W2011836736","https://openalex.org/W2096945460","https://openalex.org/W2104167780","https://openalex.org/W2104933073","https://openalex.org/W2109545609","https://openalex.org/W2132791018","https://openalex.org/W2204247986","https://openalex.org/W2251237590","https://openalex.org/W2294798173","https://openalex.org/W2343213531","https://openalex.org/W2462401346","https://openalex.org/W2521200999","https://openalex.org/W2767623047","https://openalex.org/W2911333381","https://openalex.org/W2922054794","https://openalex.org/W2936503027","https://openalex.org/W2946655868","https://openalex.org/W2953926847","https://openalex.org/W2963703197","https://openalex.org/W3015967287","https://openalex.org/W3157699413","https://openalex.org/W3202868818","https://openalex.org/W4213251304","https://openalex.org/W4214927009","https://openalex.org/W4297987590","https://openalex.org/W6603553207","https://openalex.org/W6639523096","https://openalex.org/W6648982606","https://openalex.org/W6655307070","https://openalex.org/W6677954473","https://openalex.org/W6682141768","https://openalex.org/W6685319170","https://openalex.org/W6687731691","https://openalex.org/W6718673162","https://openalex.org/W6731827338","https://openalex.org/W6745907806","https://openalex.org/W6758313303","https://openalex.org/W6758759751","https://openalex.org/W6760848272","https://openalex.org/W6762345729","https://openalex.org/W6773246680","https://openalex.org/W6774817099","https://openalex.org/W6775439456","https://openalex.org/W6777056137"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"This":[0,45],"work":[1],"explores":[2],"how":[3],"class-imbalanced":[4],"data":[5,12,21,54,109,112],"affects":[6],"deep":[7],"learning":[8,59],"and":[9,49,62],"proposes":[10],"a":[11,69,86],"balancing":[13,35],"technique":[14,130],"for":[15,22,80],"mitigation":[16],"by":[17,40,96],"generating":[18,85],"more":[19],"synthetic":[20,53,87,111],"the":[23,36,82,90,101],"minority":[24,118],"class.":[25],"In":[26,73,105],"contrast":[27],"to":[28,107],"random-based":[29],"oversampling":[30],"techniques,":[31],"our":[32,129],"approach":[33,46],"prioritizes":[34],"most":[37],"informative":[38],"region":[39,92],"finding":[41],"high":[42],"entropy":[43],"samples.":[44],"is":[47],"opportunistic":[48],"challenging":[50],"because":[51],"well-placed":[52],"points":[55],"can":[56,67],"boost":[57],"machine":[58],"algorithms\u2019":[60],"accuracy":[61],"efficiency,":[63],"whereas":[64],"poorly-placed":[65],"ones":[66],"cause":[68],"higher":[70],"misclassification":[71],"rate.":[72],"this":[74],"study,":[75],"we":[76],"present":[77],"an":[78],"algorithm":[79],"maximizing":[81,100],"probability":[83],"of":[84,93,137],"sample":[88,119],"in":[89,135],"correct":[91],"its":[94],"class":[95,102],"placing":[97],"it":[98],"toward":[99],"posterior":[103],"ratio.":[104],"addition,":[106],"preserve":[108],"topology,":[110],"are":[113],"closely":[114],"generated":[115],"within":[116],"each":[117],"neighborhood.":[120],"Overall,":[121],"experimental":[122,133],"results":[123],"on":[124],"forty-one":[125],"datasets":[126],"show":[127],"that":[128],"significantly":[131],"outperforms":[132],"methods":[134],"terms":[136],"boosting":[138],"deep-learning":[139],"performance.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2023-11-09T00:00:00"}