{"id":"https://openalex.org/W4391436932","doi":"https://doi.org/10.1186/s40537-024-00882-0","title":"A machine learning-based credit risk prediction engine system using a stacked classifier and a filter-based feature selection method","display_name":"A machine learning-based credit risk prediction engine system using a stacked classifier and a filter-based feature selection method","publication_year":2024,"publication_date":"2024-02-01","ids":{"openalex":"https://openalex.org/W4391436932","doi":"https://doi.org/10.1186/s40537-024-00882-0"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-024-00882-0","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-024-00882-0","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-024-00882-0","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-024-00882-0","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093839472","display_name":"Ileberi Emmanuel","orcid":null},"institutions":[{"id":"https://openalex.org/I24027795","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17","country_code":"ZA","type":"education","lineage":["https://openalex.org/I24027795"]}],"countries":["ZA"],"is_corresponding":true,"raw_author_name":"Ileberi Emmanuel","raw_affiliation_strings":["Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa","institution_ids":["https://openalex.org/I24027795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091303313","display_name":"Yanxia Sun","orcid":"https://orcid.org/0000-0002-3455-9625"},"institutions":[{"id":"https://openalex.org/I24027795","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17","country_code":"ZA","type":"education","lineage":["https://openalex.org/I24027795"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Yanxia Sun","raw_affiliation_strings":["Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa","institution_ids":["https://openalex.org/I24027795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100614110","display_name":"Zenghui Wang","orcid":"https://orcid.org/0000-0003-3025-336X"},"institutions":[{"id":"https://openalex.org/I165390105","display_name":"University of South Africa","ror":"https://ror.org/048cwvf49","country_code":"ZA","type":"education","lineage":["https://openalex.org/I165390105"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Zenghui Wang","raw_affiliation_strings":["Department of Electrical Engineering, University of South Africa, Johannesburg, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of South Africa, Johannesburg, South Africa","institution_ids":["https://openalex.org/I165390105"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5093839472"],"corresponding_institution_ids":["https://openalex.org/I24027795"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":55.9499,"has_fulltext":true,"cited_by_count":65,"citation_normalized_percentile":{"value":0.99951869,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"11","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9987999796867371,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9958000183105469,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9778000116348267,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7905799150466919},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7735967636108398},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6434935927391052},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6156591176986694},{"id":"https://openalex.org/keywords/computational-science-and-engineering","display_name":"Computational Science and Engineering","score":0.5969609618186951},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5694633722305298},{"id":"https://openalex.org/keywords/credit-risk","display_name":"Credit risk","score":0.4435168504714966},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.44184398651123047},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3727430999279022},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3657911419868469},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.09437310695648193},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07845297455787659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7905799150466919},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7735967636108398},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6434935927391052},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6156591176986694},{"id":"https://openalex.org/C68597687","wikidata":"https://www.wikidata.org/wiki/Q362601","display_name":"Computational Science and Engineering","level":2,"score":0.5969609618186951},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5694633722305298},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.4435168504714966},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.44184398651123047},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3727430999279022},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3657911419868469},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.09437310695648193},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07845297455787659},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1186/s40537-024-00882-0","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-024-00882-0","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-024-00882-0","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2443436568714c108ad7f76ef5f3a3ea","is_oa":false,"landing_page_url":"https://doaj.org/article/2443436568714c108ad7f76ef5f3a3ea","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 11, Iss 1, Pp 1-14 (2024)","raw_type":"article"},{"id":"pmh:oai:uir.unisa.ac.za:10500/30919","is_oa":false,"landing_page_url":"https://hdl.handle.net/10500/30919","pdf_url":null,"source":{"id":"https://openalex.org/S4306400472","display_name":"Unisa Institutional Repository (University of South Africa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165390105","host_organization_name":"University of South Africa","host_organization_lineage":["https://openalex.org/I165390105"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.1186/s40537-024-00882-0","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-024-00882-0","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-024-00882-0","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.4099999964237213,"display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G2348922789","display_name":null,"funder_award_id":"132159","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G2441575724","display_name":null,"funder_award_id":"141951","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G2811037866","display_name":null,"funder_award_id":"132159","funder_id":"https://openalex.org/F4320323959","funder_display_name":"University of Johannesburg"},{"id":"https://openalex.org/G3108406164","display_name":null,"funder_award_id":"132797","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G4379047882","display_name":null,"funder_award_id":"137951","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320323959","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4391436932.pdf"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1995258314","https://openalex.org/W2017717819","https://openalex.org/W2019454989","https://openalex.org/W2102831150","https://openalex.org/W2121082043","https://openalex.org/W2167429680","https://openalex.org/W2494454509","https://openalex.org/W2599451414","https://openalex.org/W2765811365","https://openalex.org/W2810984894","https://openalex.org/W2888680315","https://openalex.org/W2889320515","https://openalex.org/W2911964244","https://openalex.org/W2921364842","https://openalex.org/W2921539374","https://openalex.org/W2923437336","https://openalex.org/W2936086127","https://openalex.org/W2937854226","https://openalex.org/W2943529781","https://openalex.org/W2951781852","https://openalex.org/W2953030092","https://openalex.org/W2953327211","https://openalex.org/W2954085822","https://openalex.org/W2992028537","https://openalex.org/W2995579872","https://openalex.org/W3006682208","https://openalex.org/W3033640816","https://openalex.org/W3043299403","https://openalex.org/W3104830070","https://openalex.org/W3143896979","https://openalex.org/W4214489515","https://openalex.org/W4310018105"],"related_works":["https://openalex.org/W3155677752","https://openalex.org/W4390638272","https://openalex.org/W2472237121","https://openalex.org/W4323316863","https://openalex.org/W1985111449","https://openalex.org/W4304789336","https://openalex.org/W2340692695","https://openalex.org/W4306175439","https://openalex.org/W4311387186","https://openalex.org/W3014821567"],"abstract_inverted_index":{"Abstract":[0],"Credit":[1],"risk":[2,32,59],"prediction":[3,33,60],"is":[4,105,110],"a":[5,43,49],"crucial":[6],"task":[7],"for":[8],"financial":[9,36],"institutions.":[10,37],"The":[11,64,99,117,155],"technological":[12],"advancements":[13],"in":[14,35,87,107,179],"machine":[15],"learning,":[16],"coupled":[17,47],"with":[18,48,181],"the":[19,69,85,88,96,123,125,128,131,135,141,170,182,189,194],"availability":[20],"of":[21,164],"data":[22],"and":[23,79,127,151,167,173,184,197],"computing":[24],"power,":[25],"has":[26],"given":[27],"rise":[28],"to":[29,55,94,140],"more":[30],"credit":[31,58],"models":[34],"In":[38],"this":[39,108],"paper,":[40],"we":[41],"propose":[42],"stacked":[44,66,160,191],"classifier":[45,192],"approach":[46],"filter-based":[50],"feature":[51],"selection":[52],"(FS)":[53],"technique":[54],"achieve":[56],"efficient":[57],"using":[61,122],"multiple":[62],"datasets.":[63],"proposed":[65,118,190],"model":[67,161],"includes":[68],"following":[70,142],"base":[71],"estimators:":[72],"Random":[73],"Forest":[74],"(RF),":[75],"Gradient":[76,81],"Boosting":[77,82],"(GB),":[78],"Extreme":[80],"(XGB).":[83],"Furthermore,":[84,134],"estimators":[86,196],"Stacked":[89,136],"architecture":[90],"were":[91],"linked":[92],"sequentially":[93],"extract":[95],"best":[97],"performance.":[98],"filter-":[100],"based":[101,111],"FS":[102],"method":[103],"that":[104,159,188],"used":[106],"research":[109],"on":[112,169],"information":[113],"gain":[114],"(IG)":[115],"theory.":[116],"algorithm":[119,137],"was":[120,138],"evaluated":[121],"accuracy,":[124],"F1-Score":[126],"Area":[129],"Under":[130],"Curve":[132],"(AUC).":[133],"compared":[139],"methods:":[143],"Artificial":[144],"Neural":[145],"Network":[146],"(ANN),":[147],"Decision":[148],"Tree":[149],"(DT),":[150],"k-Nearest":[152],"Neighbour":[153],"(KNN).":[154],"experimental":[156],"results":[157],"show":[158],"obtained":[162],"AUCs":[163],"0.934,":[165],"0.944":[166],"0.870":[168],"Australian,":[171],"German":[172],"Taiwan":[174],"datasets,":[175],"respectively.":[176],"These":[177],"results,":[178],"conjunction":[180],"accuracy":[183],"F1-score":[185],"metrics,":[186],"demonstrated":[187],"outperforms":[193],"individual":[195],"other":[198],"existing":[199],"methods.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":17},{"year":2025,"cited_by_count":41},{"year":2024,"cited_by_count":7}],"updated_date":"2026-05-23T08:51:43.019350","created_date":"2025-10-10T00:00:00"}