{"id":"https://openalex.org/W4327710252","doi":"https://doi.org/10.1080/09540091.2023.2184310","title":"A novel bias-alleviated hybrid ensemble model based on over-sampling and post-processing for fair classification","display_name":"A novel bias-alleviated hybrid ensemble model based on over-sampling and post-processing for fair classification","publication_year":2023,"publication_date":"2023-03-17","ids":{"openalex":"https://openalex.org/W4327710252","doi":"https://doi.org/10.1080/09540091.2023.2184310"},"language":"en","primary_location":{"id":"doi:10.1080/09540091.2023.2184310","is_oa":true,"landing_page_url":"https://doi.org/10.1080/09540091.2023.2184310","pdf_url":null,"source":{"id":"https://openalex.org/S4210188800","display_name":"Connection Science","issn_l":"0954-0091","issn":["0954-0091","1360-0494"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Connection Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1080/09540091.2023.2184310","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036113889","display_name":"Fang He","orcid":"https://orcid.org/0000-0002-4185-9079"},"institutions":[{"id":"https://openalex.org/I90727586","display_name":"Zhejiang University of Finance and Economics","ror":"https://ror.org/055vj5234","country_code":"CN","type":"education","lineage":["https://openalex.org/I90727586"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fang He","raw_affiliation_strings":["School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, People\u2019s Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, People\u2019s Republic of China","institution_ids":["https://openalex.org/I90727586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101806844","display_name":"Xiaoxia Wu","orcid":"https://orcid.org/0000-0002-8549-6361"},"institutions":[{"id":"https://openalex.org/I4210156834","display_name":"Institute of Economics","ror":"https://ror.org/04v31xa23","country_code":"CN","type":"facility","lineage":["https://openalex.org/I114218197","https://openalex.org/I4210156834"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxia Wu","raw_affiliation_strings":["Department of Financial Accounting, Zhejiang Institute of Economics and Trade, Hangzhou, People\u2019s Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Financial Accounting, Zhejiang Institute of Economics and Trade, Hangzhou, People\u2019s Republic of China","institution_ids":["https://openalex.org/I4210156834"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004517421","display_name":"Wenyu Zhang","orcid":"https://orcid.org/0000-0002-8906-5411"},"institutions":[{"id":"https://openalex.org/I90727586","display_name":"Zhejiang University of Finance and Economics","ror":"https://ror.org/055vj5234","country_code":"CN","type":"education","lineage":["https://openalex.org/I90727586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyu Zhang","raw_affiliation_strings":["School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, People\u2019s Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, People\u2019s Republic of China","institution_ids":["https://openalex.org/I90727586"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101412228","display_name":"Xiaoling Huang","orcid":"https://orcid.org/0000-0002-1500-6822"},"institutions":[{"id":"https://openalex.org/I90727586","display_name":"Zhejiang University of Finance and Economics","ror":"https://ror.org/055vj5234","country_code":"CN","type":"education","lineage":["https://openalex.org/I90727586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoling Huang","raw_affiliation_strings":["Library, Zhejiang University of Finance and Economics, Hangzhou, People\u2019s Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Library, Zhejiang University of Finance and Economics, Hangzhou, People\u2019s Republic of China","institution_ids":["https://openalex.org/I90727586"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036113889"],"corresponding_institution_ids":["https://openalex.org/I90727586"],"apc_list":{"value":1270,"currency":"USD","value_usd":1270},"apc_paid":{"value":1270,"currency":"USD","value_usd":1270},"fwci":0.8694,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77282495,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"35","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.972100019454956,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9653000235557556,"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":0.7984687089920044},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.668847918510437},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6488438844680786},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6313581466674805},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6207367777824402},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5842790603637695},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46359390020370483},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45921680331230164},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4570680856704712},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4461635947227478},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.42482441663742065},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09195247292518616}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7984687089920044},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.668847918510437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6488438844680786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6313581466674805},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6207367777824402},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5842790603637695},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46359390020370483},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45921680331230164},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4570680856704712},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4461635947227478},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.42482441663742065},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09195247292518616},{"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/09540091.2023.2184310","is_oa":true,"landing_page_url":"https://doi.org/10.1080/09540091.2023.2184310","pdf_url":null,"source":{"id":"https://openalex.org/S4210188800","display_name":"Connection Science","issn_l":"0954-0091","issn":["0954-0091","1360-0494"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Connection Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f6cc57c24d6948b9bd7f48f73b030086","is_oa":true,"landing_page_url":"https://doaj.org/article/f6cc57c24d6948b9bd7f48f73b030086","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Connection Science, Vol 35, Iss 1 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/09540091.2023.2184310","is_oa":true,"landing_page_url":"https://doi.org/10.1080/09540091.2023.2184310","pdf_url":null,"source":{"id":"https://openalex.org/S4210188800","display_name":"Connection Science","issn_l":"0954-0091","issn":["0954-0091","1360-0494"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Connection Science","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5"}],"awards":[{"id":"https://openalex.org/G7534304297","display_name":null,"funder_award_id":"51875503","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W1559060276","https://openalex.org/W1678356000","https://openalex.org/W1976193075","https://openalex.org/W2101807845","https://openalex.org/W2104167780","https://openalex.org/W2104933073","https://openalex.org/W2116984840","https://openalex.org/W2130486630","https://openalex.org/W2148143831","https://openalex.org/W2157928966","https://openalex.org/W2295598076","https://openalex.org/W2317515691","https://openalex.org/W2530395818","https://openalex.org/W2559375882","https://openalex.org/W2560070550","https://openalex.org/W2593914038","https://openalex.org/W2658306063","https://openalex.org/W2761700016","https://openalex.org/W2768348081","https://openalex.org/W2897380095","https://openalex.org/W2905029197","https://openalex.org/W2910364449","https://openalex.org/W2910524267","https://openalex.org/W2919821184","https://openalex.org/W2932067728","https://openalex.org/W2942735577","https://openalex.org/W2950664378","https://openalex.org/W2962951800","https://openalex.org/W2963100392","https://openalex.org/W2963104135","https://openalex.org/W2963174898","https://openalex.org/W2974479098","https://openalex.org/W2974817986","https://openalex.org/W2989219518","https://openalex.org/W2996829667","https://openalex.org/W3008843783","https://openalex.org/W3013460382","https://openalex.org/W3029409054","https://openalex.org/W3036562957","https://openalex.org/W3038833021","https://openalex.org/W3044045296","https://openalex.org/W3045991306","https://openalex.org/W3095542981","https://openalex.org/W3101136263","https://openalex.org/W3101880446","https://openalex.org/W3113168908","https://openalex.org/W3118913631","https://openalex.org/W3120740533","https://openalex.org/W3133255363","https://openalex.org/W3134631405","https://openalex.org/W3176315233","https://openalex.org/W3181414820","https://openalex.org/W3186129612","https://openalex.org/W3200502319","https://openalex.org/W3203286693","https://openalex.org/W4206668658","https://openalex.org/W4212883601","https://openalex.org/W4289258088","https://openalex.org/W6676769703","https://openalex.org/W6728551298","https://openalex.org/W6765646913","https://openalex.org/W6788247690","https://openalex.org/W7014198846"],"related_works":["https://openalex.org/W2794896638","https://openalex.org/W1807784185","https://openalex.org/W4390905871","https://openalex.org/W3202800081","https://openalex.org/W1909207154","https://openalex.org/W3124390867","https://openalex.org/W3101614107","https://openalex.org/W3204228978","https://openalex.org/W1514365828","https://openalex.org/W4390971112"],"abstract_inverted_index":{"With":[0],"the":[1,8,12,17,25,33,48,53,63,66,94,99,116,122,139,143,146,162,168,174,178],"rapid":[2],"development":[3],"of":[4,10,36,65,121,145,167,177],"machine":[5],"learning":[6,110],"in":[7,101],"field":[9],"classification,":[11],"classification":[13,35,67,127],"fairness":[14,140,166,176],"has":[15],"become":[16],"research":[18],"emphasis":[19],"second":[20],"to":[21,52,92,114,136,160],"prediction":[22,163],"accuracy.":[23],"However,":[24],"data":[26,95],"bias":[27,96],"and":[28,59,82,103,119,141,154,165],"algorithmic":[29],"discrimination":[30],"that":[31],"affect":[32],"fair":[34],"models":[37],"have":[38],"not":[39],"been":[40],"well":[41],"resolved,":[42],"which":[43],"may":[44],"damage":[45],"or":[46],"benefit":[47],"specific":[49],"groups":[50],"related":[51],"sensitive":[54,104,152],"attributes":[55,153],"(e.g.":[56],"age,":[57],"race,":[58],"gender).":[60],"To":[61],"alleviate":[62],"unfairness":[64],"model,":[68],"this":[69],"study":[70],"proposes":[71],"a":[72,85,107,125],"novel":[73],"bias-alleviated":[74],"hybrid":[75],"ensemble":[76,109],"model":[77],"(BAHEM)":[78],"based":[79],"on":[80],"over-sampling":[81,88],"post-processing.":[83],"First,":[84],"new":[86,126],"clustering-based":[87],"method":[89,111,133],"is":[90,112,134],"proposed":[91,135],"reduce":[93],"caused":[97],"by":[98],"imbalance":[100],"label":[102],"attribute.":[105],"Then,":[106],"stacking-based":[108],"employed":[113],"obtain":[115],"higher":[117],"performance":[118],"robustness":[120],"BAHEM.":[123,147,169],"Finally,":[124],"with":[128,150,180],"alternating":[129],"normalisation":[130],"(CAN)-based":[131],"post-processing":[132],"further":[137],"improve":[138],"maintain":[142],"accuracy":[144,164,182],"Three":[148],"datasets":[149],"different":[151],"four":[155],"evaluation":[156],"metrics":[157],"were":[158],"used":[159],"evaluate":[161],"The":[170],"experimental":[171],"results":[172],"verify":[173],"superior":[175],"BAHEM":[179],"little":[181],"reduction.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-14T06:11:07.267592","created_date":"2025-10-10T00:00:00"}
