{"id":"https://openalex.org/W4306152303","doi":"https://doi.org/10.1111/coin.12555","title":"An efficient credit card fraud detection approach using cost\u2010sensitive weak learner with imbalanced dataset","display_name":"An efficient credit card fraud detection approach using cost\u2010sensitive weak learner with imbalanced dataset","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4306152303","doi":"https://doi.org/10.1111/coin.12555"},"language":"en","primary_location":{"id":"doi:10.1111/coin.12555","doi":"https://doi.org/10.1111/coin.12555","is_oa":false,"landing_page_url":"https://doi.org/10.1111/coin.12555","pdf_url":null,"source":{"id":"https://openalex.org/S56561474","display_name":"Computational Intelligence","issn_l":"0824-7935","issn":["0824-7935","1467-8640"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-c","cwts-core","dongbi-d","jufo-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W4306152303"],"child_works_count":0,"type":"article","is_traditional":true,"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/A5102983980","display_name":"Ajeet Singh","orcid":"https://orcid.org/0000-0001-5409-8443","observed_orcids":["https://orcid.org/0000-0001-5409-8443"]},"institutions":[{"id":"https://openalex.org/I105454292","display_name":"Guru Gobind Singh Indraprastha University","ror":"https://ror.org/034q1za58","country_code":"IN","type":"education","lineage":["https://openalex.org/I105454292"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ajeet Singh","raw_affiliation_strings":["University School of Information, Communication & Technology Guru Gobind Singh Indraprastha University Delhi India"],"raw_orcid":"https://orcid.org/0000-0001-5409-8443","affiliations":[{"raw_affiliation_string":"University School of Information, Communication & Technology Guru Gobind Singh Indraprastha University Delhi India","institution_ids":["https://openalex.org/I105454292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050998276","display_name":"Anurag Jain","orcid":"https://orcid.org/0000-0001-6804-2758","observed_orcids":["https://orcid.org/0000-0001-6804-2758"]},"institutions":[{"id":"https://openalex.org/I105454292","display_name":"Guru Gobind Singh Indraprastha University","ror":"https://ror.org/034q1za58","country_code":"IN","type":"education","lineage":["https://openalex.org/I105454292"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anurag Jain","raw_affiliation_strings":["University School of Information, Communication & Technology Guru Gobind Singh Indraprastha University Delhi India"],"raw_orcid":"https://orcid.org/0000-0001-6804-2758","affiliations":[{"raw_affiliation_string":"University School of Information, Communication & Technology Guru Gobind Singh Indraprastha University Delhi India","institution_ids":["https://openalex.org/I105454292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102983980"],"corresponding_institution_ids":["https://openalex.org/I105454292"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":null,"fwci":1.0622,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.76291151,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"38","issue":"6","first_page":"2035","last_page":"2055"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","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/T11652","display_name":"Imbalanced Data Classification Techniques","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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.0,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/credit-card-fraud-detection","display_name":"credit card fraud detection","score":1.0},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.7519999742507935},{"id":"https://openalex.org/keywords/cost-sensitive-learning","display_name":"cost-sensitive learning","score":0.9980000257492065},{"id":"https://openalex.org/keywords/bagging","display_name":"bagging","score":0.9750000238418579},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9909999966621399},{"id":"https://openalex.org/keywords/fraudulent-transaction-classification","display_name":"fraudulent transaction classification","score":0.6399999856948853}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7931195497512817},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.767952561378479},{"id":"https://openalex.org/C2780747020","wikidata":"https://www.wikidata.org/wiki/Q83873","display_name":"Credit card fraud","level":4,"score":0.7393375039100647},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.7303670644760132},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5990403890609741},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5796395540237427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5439096093177795},{"id":"https://openalex.org/C2779308522","wikidata":"https://www.wikidata.org/wiki/Q843958","display_name":"Digitization","level":2,"score":0.4317007064819336},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42585843801498413},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4167479872703552},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1111/coin.12555","doi":"https://doi.org/10.1111/coin.12555","is_oa":false,"landing_page_url":"https://doi.org/10.1111/coin.12555","pdf_url":null,"source":{"id":"https://openalex.org/S56561474","display_name":"Computational Intelligence","issn_l":"0824-7935","issn":["0824-7935","1467-8640"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-c","cwts-core","dongbi-d","jufo-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W435157458","https://openalex.org/W769353746","https://openalex.org/W1563938718","https://openalex.org/W1583721256","https://openalex.org/W1766838353","https://openalex.org/W1819386543","https://openalex.org/W2032435122","https://openalex.org/W2038136632","https://openalex.org/W2096945460","https://openalex.org/W2099454382","https://openalex.org/W2133064331","https://openalex.org/W2148125134","https://openalex.org/W2148143831","https://openalex.org/W2533835508","https://openalex.org/W2557636016","https://openalex.org/W2591994479","https://openalex.org/W2610381844","https://openalex.org/W2759501326","https://openalex.org/W2784378279","https://openalex.org/W2785637175","https://openalex.org/W2789081542","https://openalex.org/W2805223094","https://openalex.org/W2806534322","https://openalex.org/W2807938290","https://openalex.org/W2809214597","https://openalex.org/W2912934387","https://openalex.org/W2942334416","https://openalex.org/W2942805196","https://openalex.org/W2946774663","https://openalex.org/W2960423448","https://openalex.org/W2965897052","https://openalex.org/W2972176792","https://openalex.org/W2975741303","https://openalex.org/W2978488090","https://openalex.org/W2989446870","https://openalex.org/W2991266736","https://openalex.org/W2991630585","https://openalex.org/W2998046181","https://openalex.org/W3008864669","https://openalex.org/W3025767205","https://openalex.org/W3038539283","https://openalex.org/W3084383304","https://openalex.org/W3096733902","https://openalex.org/W3120655457","https://openalex.org/W3128553169","https://openalex.org/W3202026189","https://openalex.org/W4212968553"],"related_works":["https://openalex.org/W3084383304","https://openalex.org/W2998046181","https://openalex.org/W7163026667","https://openalex.org/W4403826670","https://openalex.org/W3117060406","https://openalex.org/W4386960695","https://openalex.org/W2958026736","https://openalex.org/W3002992314","https://openalex.org/W3082225844","https://openalex.org/W4391430106"],"abstract_inverted_index":{"Abstract":[0],"The":[1,48,99,156,184,195],"usage":[2],"of":[3,21,50],"credit":[4,23,38,168],"cards":[5],"is":[6,32,45,66,103],"increasing":[7],"in":[8,36,105],"the":[9,19,37,42,95,123,127,141,149,166,200],"digitization":[10],"era":[11],"to":[12,57,68,89,122,152,206],"purchase":[13],"goods":[14],"online":[15],"or":[16],"offline,":[17],"and":[18,84,93,131,160,180,192],"number":[20],"fraudulent":[22,124],"card":[24,39,169],"transactions":[25,125],"has":[26,54,145,203],"also":[27],"increased.":[28],"Fraudulent":[29],"transaction":[30],"detection":[31,64],"a":[33,61,82,136],"big":[34],"issue":[35],"domain":[40],"because":[41],"data":[43,170],"set":[44],"highly":[46],"imbalanced.":[47],"performance":[49],"traditional":[51],"classification":[52,154],"techniques":[53],"decreased":[55],"due":[56],"this":[58,70],"issue.":[59,71,98],"Therefore,":[60,140],"dynamic":[62],"fraud":[63],"system":[65],"required":[67],"overcome":[69,94],"This":[72],"article":[73],"proposes":[74],"an":[75,106],"efficient":[76],"cost\u2010sensitive":[77,128],"weak":[78,113],"learner":[79,114],"approach":[80,102,187,202],"with":[81,111,177],"bagging":[83,137],"random":[85,142],"forest":[86,143],"classifier":[87,130,144],"(CSWLB)":[88],"minimize":[90],"misclassification":[91],"problems":[92],"class":[96],"imbalance":[97],"proposed":[100,157,185,201],"CSWLB":[101,186],"included":[104],"adaptive":[107],"algorithmic":[108],"method":[109],"(cost\u2010sensitive)":[110],"two":[112],"ensembles":[115],"(bagging\u2010random":[116],"forest),":[117],"which":[118],"assigns":[119],"high":[120],"weight":[121,133,150],"using":[126,135],"learning":[129,182],"generates":[132],"bags":[134,151],"ensemble":[138],"classifier.":[139],"been":[146,163,175],"implemented":[147],"on":[148,165],"improve":[153],"accuracy.":[155,194],"approach's":[158],"effectiveness":[159],"efficiency":[161],"have":[162,174],"computed":[164],"Brazilian":[167],"set.":[171],"Its":[172],"results":[173,197],"compared":[176,205],"sampling,":[178],"cost\u2010sensitive,":[179],"machine":[181],"techniques.":[183,208],"obtained":[188],"765":[189],"total":[190],"costs":[191],"97.361%":[193],"experimental":[196],"show":[198],"that":[199],"outperformed":[204],"other":[207]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2022-10-14T00:00:00"}
