{"id":"https://openalex.org/W4366517575","doi":"https://doi.org/10.1145/3582099.3582130","title":"Forecasting Credit Card Defaults Using Light Gradient Boosting Machine with Dart Algorithm","display_name":"Forecasting Credit Card Defaults Using Light Gradient Boosting Machine with Dart Algorithm","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4366517575","doi":"https://doi.org/10.1145/3582099.3582130"},"language":"en","primary_location":{"id":"doi:10.1145/3582099.3582130","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3582099.3582130","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","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/A5091717539","display_name":"Haoming Wang","orcid":"https://orcid.org/0000-0001-6502-9988"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haoming Wang","raw_affiliation_strings":["Southwestern University of Finance and Economics, China"],"raw_orcid":"https://orcid.org/0000-0001-6502-9988","affiliations":[{"raw_affiliation_string":"Southwestern University of Finance and Economics, China","institution_ids":["https://openalex.org/I204831749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5091717539"],"corresponding_institution_ids":["https://openalex.org/I204831749"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"207","last_page":"212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.973800003528595,"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":0.973800003528595,"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":0.9736999869346619,"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"}},{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.968999981880188,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.826524019241333},{"id":"https://openalex.org/keywords/default","display_name":"Default","score":0.7047953009605408},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6901082992553711},{"id":"https://openalex.org/keywords/credit-card","display_name":"Credit card","score":0.6733590364456177},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6487041711807251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6232297420501709},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.494750440120697},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4395962953567505},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39278507232666016},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.2742917537689209},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.21725982427597046},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.18239110708236694}],"concepts":[{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.826524019241333},{"id":"https://openalex.org/C69637215","wikidata":"https://www.wikidata.org/wiki/Q702362","display_name":"Default","level":2,"score":0.7047953009605408},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6901082992553711},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.6733590364456177},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6487041711807251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6232297420501709},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.494750440120697},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4395962953567505},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39278507232666016},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2742917537689209},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.21725982427597046},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.18239110708236694},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3582099.3582130","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3582099.3582130","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.49000000953674316,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1775823180","https://openalex.org/W2194775991","https://openalex.org/W2295598076","https://openalex.org/W2767241473","https://openalex.org/W2787894218"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W3094138326","https://openalex.org/W4310224730"],"abstract_inverted_index":{"With":[0],"the":[1,32,88,100,107,146,152,161,178],"rapid":[2],"development":[3,34],"of":[4,35,45,87,155],"financial":[5,36,51],"services":[6],"and":[7,19,53,97,119,133,149,171],"technologies,":[8],"credit":[9,27,41,73],"cards":[10],"have":[11],"been":[12],"increasingly":[13],"used":[14,135],"for":[15],"personal":[16],"daily":[17],"consumption":[18],"small":[20],"loans.":[21],"However,":[22],"bad":[23],"debts":[24],"caused":[25],"by":[26,103],"card":[28,42,74],"defaults":[29,43,75],"remarkably":[30],"affect":[31],"healthy":[33],"markets.":[37],"Therefore,":[38],"forecasting":[39],"potential":[40],"is":[44,81,95],"great":[46],"significance":[47],"with":[48,123,130,141],"respect":[49],"to":[50,71,136],"stability":[52],"economic":[54],"order.":[55],"For":[56],"this":[57,77],"purpose,":[58],"we":[59],"propose":[60],"a":[61,174],"machine":[62],"learning":[63],"method":[64,157],"based":[65],"on":[66,116,160],"Light":[67],"Gradient":[68],"Boosting":[69],"Machine":[70],"detect":[72],"in":[76,83,106,177],"paper.":[78],"DART":[79],"algorithm":[80],"utilized":[82],"our":[84,156],"model":[85,94,147],"instead":[86],"traditional":[89],"gradient":[90],"boosting":[91],"tree.":[92],"The":[93],"trained":[96],"evaluated":[98],"using":[99],"dataset":[101],"provided":[102],"American":[104,110],"Express":[105,111],"Kaggle":[108,179],"competition":[109,153],"-":[112],"Default":[113],"Prediction.":[114],"Based":[115],"feature":[117],"analysis":[118],"engineering,":[120],"raw":[121],"data":[122,129],"190":[124],"descriptors":[125],"are":[126,134],"transformed":[127],"into":[128],"2358":[131],"descriptors,":[132],"train":[137],"3":[138],"LightGBM":[139],"models":[140],"different":[142],"hyper-parameters.":[143],"By":[144],"applying":[145],"ensemble":[148],"pseudo-label":[150],"technique,":[151],"metric":[154],"reaches":[158],"0.80029/0.80767":[159],"public/private":[162],"test":[163],"set.":[164],"This":[165],"score":[166],"ranks":[167],"106/4874":[168],"(top":[169],"2.2%),":[170],"can":[172],"get":[173],"silver":[175],"medal":[176],"competition.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}