{"id":"https://openalex.org/W30388287","doi":"https://doi.org/10.2495/data020721","title":"Comparative performance of credit scoring models using aggregated predictors","display_name":"Comparative performance of credit scoring models using aggregated predictors","publication_year":2002,"publication_date":"2002-09-10","ids":{"openalex":"https://openalex.org/W30388287","doi":"https://doi.org/10.2495/data020721","mag":"30388287"},"language":"en","primary_location":{"id":"pmh:oai:art.torvergata.it:2108/53332","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/2108/53332","pdf_url":"http://hdl.handle.net/2108/53332","source":{"id":"https://openalex.org/S4306400993","display_name":"Cineca Institutional Research Information System (Tor Vergata University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I116067653","host_organization_name":"University of Rome Tor Vergata","host_organization_lineage":["https://openalex.org/I116067653"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/2108/53332","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081034012","display_name":"Stefano Caiazza","orcid":"https://orcid.org/0000-0003-2138-929X","observed_orcids":["https://orcid.org/0000-0003-2138-929X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stefano Caiazza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5033566252","display_name":"Simone Borra","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Simone Borra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.1311,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.93589744,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"747","last_page":"756"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9887999892234802,"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":0.9887999892234802,"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":0.007499999832361937,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/credit-scoring","display_name":"credit scoring","score":1.0},{"id":"https://openalex.org/keywords/misclassification-rate","display_name":"misclassification rate","score":0.7549999952316284},{"id":"https://openalex.org/keywords/logit-model","display_name":"logit model","score":0.8109999895095825},{"id":"https://openalex.org/keywords/classification-trees","display_name":"classification trees","score":0.8399999737739563},{"id":"https://openalex.org/keywords/cart","display_name":"CART","score":0.8709999918937683},{"id":"https://openalex.org/keywords/bagging","display_name":"bagging","score":0.9309999942779541},{"id":"https://openalex.org/keywords/boosting","display_name":"boosting","score":1.0},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.640999972820282},{"id":"https://openalex.org/keywords/predictive-performance","display_name":"predictive performance","score":0.460999995470047}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8348104953765869},{"id":"https://openalex.org/C2777275308","wikidata":"https://www.wikidata.org/wiki/Q234668","display_name":"Cart","level":2,"score":0.8171526789665222},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6692284345626831},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5540739297866821},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5409276485443115},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5330063104629517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49474188685417175},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4856339991092682},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4831632971763611},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4563785493373871},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4450944662094116},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4346778392791748},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4230441153049469},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3798828721046448},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.37264275550842285},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2870541214942932},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16557559370994568},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08010199666023254},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:art.torvergata.it:2108/53332","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/2108/53332","pdf_url":"http://hdl.handle.net/2108/53332","source":{"id":"https://openalex.org/S4306400993","display_name":"Cineca Institutional Research Information System (Tor Vergata University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I116067653","host_organization_name":"University of Rome Tor Vergata","host_organization_lineage":["https://openalex.org/I116067653"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"mag:30388287","doi":"https://doi.org/10.2495/data020721","is_oa":false,"landing_page_url":"https://www.witpress.com/Secure/elibrary/papers/DATA02/DATA02072FU.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Data Mining","raw_type":null}],"best_oa_location":{"id":"pmh:oai:art.torvergata.it:2108/53332","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/2108/53332","pdf_url":"http://hdl.handle.net/2108/53332","source":{"id":"https://openalex.org/S4306400993","display_name":"Cineca Institutional Research Information System (Tor Vergata University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I116067653","host_organization_name":"University of Rome Tor Vergata","host_organization_lineage":["https://openalex.org/I116067653"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W30388287.pdf"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W1497131193","https://openalex.org/W1594031697","https://openalex.org/W1988790447","https://openalex.org/W2026301198","https://openalex.org/W2070493638","https://openalex.org/W2172195373","https://openalex.org/W2477400917"],"related_works":["https://openalex.org/W2044804342","https://openalex.org/W6990062409","https://openalex.org/W4309456838","https://openalex.org/W1971547695","https://openalex.org/W1827583191","https://openalex.org/W1664147347","https://openalex.org/W2904639065","https://openalex.org/W7096169924","https://openalex.org/W2039405854","https://openalex.org/W2366162553"],"abstract_inverted_index":{"The":[0],"aim":[1],"of":[2,12,15,33,55,84,94,100],"this":[3,43,56],"paper":[4],"is":[5],"to":[6,30,45,69],"evaluate":[7,46,70],"the":[8,31,47,52,97],"results":[9,34,88],"in":[10,24,82],"term":[11,83],"misclassification":[13],"rate":[14,99],"two":[16,57,63],"classification":[17,85,92],"models,":[18,58],"Logit":[19],"and":[20,67,96],"Classification":[21],"Trees":[22],"(Cart),":[23],"a":[25,79,90],"credit":[26],"scoring":[27],"context.":[28],"Due":[29],"dependence":[32],"on":[35],"input":[36],"variables":[37,113],"we":[38,59],"will":[39],"take":[40],"into":[41],"account":[42],"aspect":[44],"prediction":[48,53],"performance.":[49],"To":[50],"improve":[51],"capability":[54,93],"have":[60],"also":[61],"applied":[62],"statistical":[64],"techniques,":[65],"bagging":[66],"boosting,":[68],"whether":[71],"using":[72,107],"these":[73],"aggregated":[74,108],"predictors":[75],"can":[76,103],"be":[77,104],"reached":[78],"better":[80,91],"performance":[81],"results.":[86],"Our":[87],"indicate":[89],"Cart":[95,111],"error":[98],"both":[101],"models":[102],"further":[105],"reduced":[106],"predictors.":[109],"Furthermore":[110],"avoids":[112],"selection":[114],"problem.":[115]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2016-06-24T00:00:00"}
