{"id":"https://openalex.org/W4407721247","doi":"https://doi.org/10.1007/s10479-025-06494-y","title":"Ensemble learning algorithms based on easyensemble sampling for financial distress prediction","display_name":"Ensemble learning algorithms based on easyensemble sampling for financial distress prediction","publication_year":2025,"publication_date":"2025-02-18","ids":{"openalex":"https://openalex.org/W4407721247","doi":"https://doi.org/10.1007/s10479-025-06494-y"},"language":"en","primary_location":{"id":"doi:10.1007/s10479-025-06494-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10479-025-06494-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10479-025-06494-y.pdf","source":{"id":"https://openalex.org/S57667410","display_name":"Annals of Operations Research","issn_l":"0254-5330","issn":["0254-5330","1572-9338"],"is_oa":false,"is_in_doaj":false,"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":"Annals of Operations Research","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10479-025-06494-y.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084377431","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0002-3195-3379"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Department of Economics, Graduate School of Humanities and Social Sciences, Hiroshima University, Higashihiroshima, Japan"],"raw_orcid":"https://orcid.org/0000-0002-3195-3379","affiliations":[{"raw_affiliation_string":"Department of Economics, Graduate School of Humanities and Social Sciences, Hiroshima University, Higashihiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035977104","display_name":"Yoshihisa Suzuki","orcid":"https://orcid.org/0000-0003-2083-5489"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshihisa Suzuki","raw_affiliation_strings":["Department of Economics, Graduate School of Humanities and Social Sciences, Hiroshima University, Higashihiroshima, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Economics, Graduate School of Humanities and Social Sciences, Hiroshima University, Higashihiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014774313","display_name":"Shuyi Du","orcid":"https://orcid.org/0000-0002-0352-856X"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyi Du","raw_affiliation_strings":["School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084377431"],"corresponding_institution_ids":["https://openalex.org/I113306721"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":36.7487,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.99763931,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"346","issue":"3","first_page":"2141","last_page":"2172"},"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":0.9829000234603882,"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/T12394","display_name":"Insurance and Financial Risk Management","score":0.9433000087738037,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/financial-distress","display_name":"Financial distress","score":0.6572078466415405},{"id":"https://openalex.org/keywords/theory-of-computation","display_name":"Theory of computation","score":0.65328049659729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5864576101303101},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5619734525680542},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.5026865005493164},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4693022072315216},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4488527774810791},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38677453994750977},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3822619318962097},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.1348661482334137}],"concepts":[{"id":"https://openalex.org/C2984760201","wikidata":"https://www.wikidata.org/wiki/Q1785212","display_name":"Financial distress","level":2,"score":0.6572078466415405},{"id":"https://openalex.org/C24858836","wikidata":"https://www.wikidata.org/wiki/Q844718","display_name":"Theory of computation","level":2,"score":0.65328049659729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5864576101303101},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5619734525680542},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.5026865005493164},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4693022072315216},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4488527774810791},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38677453994750977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3822619318962097},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.1348661482334137},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C73283319","wikidata":"https://www.wikidata.org/wiki/Q1416617","display_name":"Financial system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10479-025-06494-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10479-025-06494-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10479-025-06494-y.pdf","source":{"id":"https://openalex.org/S57667410","display_name":"Annals of Operations Research","issn_l":"0254-5330","issn":["0254-5330","1572-9338"],"is_oa":false,"is_in_doaj":false,"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":"Annals of Operations Research","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:spr:annopr:v:346:y:2025:i:3:d:10.1007_s10479-025-06494-y","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s10479-025-06494-y","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s10479-025-06494-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10479-025-06494-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10479-025-06494-y.pdf","source":{"id":"https://openalex.org/S57667410","display_name":"Annals of Operations Research","issn_l":"0254-5330","issn":["0254-5330","1572-9338"],"is_oa":false,"is_in_doaj":false,"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":"Annals of Operations Research","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322166","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4407721247.pdf"},"referenced_works_count":74,"referenced_works":["https://openalex.org/W38296636","https://openalex.org/W1494192115","https://openalex.org/W1510052597","https://openalex.org/W1571836963","https://openalex.org/W1596717185","https://openalex.org/W1678356000","https://openalex.org/W1797640679","https://openalex.org/W1977117871","https://openalex.org/W1987054329","https://openalex.org/W1988518729","https://openalex.org/W1988790447","https://openalex.org/W2020848494","https://openalex.org/W2038565708","https://openalex.org/W2047616382","https://openalex.org/W2048801439","https://openalex.org/W2055303666","https://openalex.org/W2056614917","https://openalex.org/W2064031858","https://openalex.org/W2070493638","https://openalex.org/W2079402140","https://openalex.org/W2085083199","https://openalex.org/W2095649738","https://openalex.org/W2102636708","https://openalex.org/W2103291381","https://openalex.org/W2105210673","https://openalex.org/W2115682519","https://openalex.org/W2119525622","https://openalex.org/W2124532504","https://openalex.org/W2135125546","https://openalex.org/W2135293965","https://openalex.org/W2137959503","https://openalex.org/W2143426320","https://openalex.org/W2148143831","https://openalex.org/W2149298154","https://openalex.org/W2149723649","https://openalex.org/W2165035706","https://openalex.org/W2168020168","https://openalex.org/W2169139688","https://openalex.org/W2180748755","https://openalex.org/W2295598076","https://openalex.org/W2319270064","https://openalex.org/W2498119267","https://openalex.org/W2586297576","https://openalex.org/W2588836480","https://openalex.org/W2602458379","https://openalex.org/W2606916050","https://openalex.org/W2611119646","https://openalex.org/W2800788706","https://openalex.org/W2802643674","https://openalex.org/W2808349803","https://openalex.org/W2889397843","https://openalex.org/W2911964244","https://openalex.org/W2959801916","https://openalex.org/W2962981269","https://openalex.org/W3082648102","https://openalex.org/W3086007433","https://openalex.org/W3105609465","https://openalex.org/W3124240175","https://openalex.org/W3134611816","https://openalex.org/W3215669537","https://openalex.org/W4206801262","https://openalex.org/W4212883601","https://openalex.org/W4220900187","https://openalex.org/W4235507090","https://openalex.org/W4239510810","https://openalex.org/W4255375128","https://openalex.org/W4255427581","https://openalex.org/W4281490929","https://openalex.org/W4367394041","https://openalex.org/W4388297464","https://openalex.org/W6606879723","https://openalex.org/W6684782521","https://openalex.org/W6808111085","https://openalex.org/W6821113053"],"related_works":["https://openalex.org/W2064563243","https://openalex.org/W4281976195","https://openalex.org/W4250775169","https://openalex.org/W4404964782","https://openalex.org/W3034338377","https://openalex.org/W4309231192","https://openalex.org/W2373792516","https://openalex.org/W2364654346","https://openalex.org/W2538183362","https://openalex.org/W3124028778"],"abstract_inverted_index":{"Abstract":[0],"Ensemble":[1],"learning":[2,42],"algorithms":[3],"show":[4,50,79],"good":[5],"forecasting":[6,56],"performances":[7],"for":[8],"financial":[9,46,123],"distress":[10],"in":[11,121],"many":[12],"studies.":[13],"Despite":[14],"considering":[15],"the":[16,31,76,81,88,94,98,109],"feature":[17,20,82],"selection":[18,83],"and":[19,37,71,75,116],"importance":[21],"procedures,":[22],"most":[23],"overlook":[24],"imbalanced":[25],"data":[26],"handling.":[27],"This":[28],"study":[29],"proposes":[30],"Easyensemble":[32,52],"method":[33],"based":[34],"on":[35],"undersampling":[36],"combines":[38],"it":[39],"with":[40],"ensemble":[41],"models":[43],"to":[44],"predict":[45],"distress.":[47,124],"The":[48],"results":[49,78],"that":[51,80],"sampling":[53],"presents":[54],"better":[55],"performance":[57],"than":[58],"SMOTE":[59],"sampling.":[60],"We":[61],"subsequently":[62],"conduct":[63],"Permutation":[64],"Importance":[65],"(PIMP),":[66],"Recursive":[67],"Feature":[68],"Elimination":[69],"(RFE),":[70],"partial":[72],"dependence":[73],"plots,":[74],"experimental":[77],"procedure":[84],"can":[85],"effectively":[86],"reduce":[87],"number":[89],"of":[90],"indicators":[91,110],"without":[92],"affecting":[93],"prediction":[95,99],"accuracy,":[96],"improve":[97],"efficiency":[100],"as":[101,103],"well":[102],"save":[104],"processing":[105],"time.":[106],"In":[107],"addition,":[108],"from":[111],"profitability,":[112],"cash":[113],"flow,":[114],"solvency,":[115],"structural":[117],"ratios":[118],"are":[119],"essential":[120],"predicting":[122]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":7}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}