{"id":"https://openalex.org/W3112634193","doi":"https://doi.org/10.1109/smc42975.2020.9283116","title":"A two-level sampling strategy for pruning methods applied to credit scoring","display_name":"A two-level sampling strategy for pruning methods applied to credit scoring","publication_year":2020,"publication_date":"2020-10-11","ids":{"openalex":"https://openalex.org/W3112634193","doi":"https://doi.org/10.1109/smc42975.2020.9283116","mag":"3112634193"},"language":"en","primary_location":{"id":"doi:10.1109/smc42975.2020.9283116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9283116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","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/A5030872353","display_name":"Luiz Vieira Filho","orcid":"https://orcid.org/0000-0002-9047-8483"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Luiz Vieira e Silva Filho","raw_affiliation_strings":["Centro de Inform\u00e1tica, Universidade Federal de Pernambuco (UFPE), Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco (UFPE), Recife, Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112764995","display_name":"George DC Cavalcanti","orcid":null},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"George DC Cavalcanti","raw_affiliation_strings":["Centro de Inform\u00e1tica, Universidade Federal de Pernambuco (UFPE), Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco (UFPE), Recife, Brazil","institution_ids":["https://openalex.org/I25112270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030872353"],"corresponding_institution_ids":["https://openalex.org/I25112270"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17003384,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"97","issue":null,"first_page":"96","last_page":"101"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9990000128746033,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.996399998664856,"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/computer-science","display_name":"Computer science","score":0.7856433391571045},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7230855226516724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.644618034362793},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5705977082252502},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5621989965438843},{"id":"https://openalex.org/keywords/strengths-and-weaknesses","display_name":"Strengths and weaknesses","score":0.5608218908309937},{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.5211784243583679},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5143902897834778},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46548786759376526},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.42331740260124207},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38184654712677},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07320019602775574},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.06779620051383972}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7856433391571045},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7230855226516724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.644618034362793},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5705977082252502},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5621989965438843},{"id":"https://openalex.org/C63882131","wikidata":"https://www.wikidata.org/wiki/Q17122954","display_name":"Strengths and weaknesses","level":2,"score":0.5608218908309937},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.5211784243583679},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5143902897834778},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46548786759376526},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.42331740260124207},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38184654712677},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07320019602775574},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.06779620051383972},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc42975.2020.9283116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9283116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1562197959","https://openalex.org/W1577983635","https://openalex.org/W1590261618","https://openalex.org/W1676820704","https://openalex.org/W1973996862","https://openalex.org/W1984533548","https://openalex.org/W1988790447","https://openalex.org/W1995276998","https://openalex.org/W2019778169","https://openalex.org/W2038705219","https://openalex.org/W2056221673","https://openalex.org/W2093717447","https://openalex.org/W2100128988","https://openalex.org/W2103346566","https://openalex.org/W2113242816","https://openalex.org/W2124868070","https://openalex.org/W2125555508","https://openalex.org/W2150757437","https://openalex.org/W2167819460","https://openalex.org/W2273278181","https://openalex.org/W2487087946","https://openalex.org/W2724383523","https://openalex.org/W2755371172","https://openalex.org/W2887979259","https://openalex.org/W2912934387","https://openalex.org/W3004732066","https://openalex.org/W3120740533","https://openalex.org/W4212883601","https://openalex.org/W6633883679"],"related_works":["https://openalex.org/W2521519254","https://openalex.org/W4295769391","https://openalex.org/W3139833644","https://openalex.org/W1980381208","https://openalex.org/W2972220648","https://openalex.org/W3123110765","https://openalex.org/W4383553409","https://openalex.org/W2104948296","https://openalex.org/W1735800226","https://openalex.org/W2332667808"],"abstract_inverted_index":{"Multiple":[0],"Classifiers":[1],"Systems":[2],"(MCS)":[3],"are":[4,49,62],"based":[5],"on":[6,43],"the":[7,10,13,44,65,73,104,109,114,117,120,123,132,148,152,160,180,185],"idea":[8],"that":[9,61,89,151],"combination":[11],"of":[12,15,72,131,179],"opinion":[14],"several":[16],"experts":[17],"can":[18],"generate":[19,77],"better":[20,156],"results":[21,149,168],"than":[22,159],"when":[23],"only":[24,108,127],"one":[25,36],"expert":[26],"is":[27,75,135,140],"used.":[28],"Several":[29],"MCS":[30,118],"techniques":[31],"have":[32],"been":[33],"developed;":[34],"each":[35],"has":[37],"its":[38],"strengths":[39],"and":[40,86,122,147,164],"weaknesses":[41],"depending":[42],"context":[45],"in":[46,92,142,184],"which":[47],"they":[48],"applied.":[50],"This":[51],"work":[52,90],"presents":[53],"a":[54,78,96,100,128,143],"two-level":[55],"sampling":[56,83],"strategy":[57],"for":[58],"pruning":[59,101],"methods":[60],"applied":[63],"to":[64,76,94],"credit":[66,144],"scoring":[67,145],"task.":[68],"The":[69,137],"first":[70],"step":[71],"proposal":[74,115],"pool":[79,106,134],"using":[80],"two":[81],"well-known":[82],"methods,":[84],"bagging":[85],"random":[87],"subspace,":[88],"complementarity":[91],"order":[93],"produce":[95],"diverse":[97],"pool.":[98],"After,":[99],"method":[102],"reduces":[103],"generated":[105,183],"maintaining":[107],"most":[110],"competent":[111],"classifiers.":[112],"So,":[113],"improves":[116],"regarding":[119],"accuracy":[121,157],"computational":[124],"effort,":[125],"since":[126],"small":[129],"percentage":[130],"original":[133,181],"stored.":[136],"proposed":[138,153],"architecture":[139,154],"evaluated":[141],"application,":[146],"showed":[150],"obtained":[155,171],"rates":[158],"single":[161],"best":[162],"approach":[163],"literature":[165],"methods.":[166],"These":[167],"were":[169,176],"also":[170],"with":[172],"ensembles":[173],"whose":[174],"sizes":[175],"around":[177],"20%":[178],"pools":[182],"training":[186],"phase.":[187]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
