{"id":"https://openalex.org/W4406459149","doi":"https://doi.org/10.1109/bigdata62323.2024.10825524","title":"A Beam-Search Based Method to Select Classification and Imputation Methods for Fair and Accurate Data Analysis","display_name":"A Beam-Search Based Method to Select Classification and Imputation Methods for Fair and Accurate Data Analysis","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406459149","doi":"https://doi.org/10.1109/bigdata62323.2024.10825524"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825524","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825524","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5115904652","display_name":"Dodavah Mowoh","orcid":null},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dodavah Mowoh","raw_affiliation_strings":["University of Maryland-Baltimore County,Information Systems Department,Baltimore,Maryland,21250-0002"],"affiliations":[{"raw_affiliation_string":"University of Maryland-Baltimore County,Information Systems Department,Baltimore,Maryland,21250-0002","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100438517","display_name":"Zhiyuan Chen","orcid":"https://orcid.org/0000-0002-9828-5272"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiyuan Chen","raw_affiliation_strings":["University of Maryland-Baltimore County,Information Systems Department,Baltimore,Maryland,21250-0002"],"affiliations":[{"raw_affiliation_string":"University of Maryland-Baltimore County,Information Systems Department,Baltimore,Maryland,21250-0002","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5115904652"],"corresponding_institution_ids":["https://openalex.org/I79272384"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30989241,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5281","last_page":"5288"},"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":0.9980999827384949,"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":0.9980999827384949,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9754999876022339,"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":0.9437999725341797,"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":0.6815887093544006},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6201912760734558},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5193530321121216},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.258360356092453},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.1453651487827301}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6815887093544006},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6201912760734558},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5193530321121216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.258360356092453},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.1453651487827301}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825524","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825524","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2932881901","https://openalex.org/W2990138404","https://openalex.org/W3045004532","https://openalex.org/W3092541244","https://openalex.org/W3120740533","https://openalex.org/W3136824354","https://openalex.org/W3169530247","https://openalex.org/W3181414820","https://openalex.org/W4241162727","https://openalex.org/W4288758404","https://openalex.org/W4318433433","https://openalex.org/W4388229648"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4211215373","https://openalex.org/W4396701345","https://openalex.org/W3217094455","https://openalex.org/W2376932109"],"abstract_inverted_index":{"Members":[0],"from":[1],"disadvantaged":[2],"or":[3],"minority":[4],"groups":[5],"are":[6],"often":[7],"more":[8],"likely":[9],"to":[10,22,46,59,70,80,99,119,136,144],"have":[11,35],"missing":[12,25],"values":[13,26],"in":[14],"their":[15],"record.":[16],"Imputation":[17],"is":[18,30,67],"a":[19,115],"common":[20],"approach":[21,91],"deal":[23],"with":[24,44,160],"before":[27],"the":[28,72,82,121,138,141,151,155],"data":[29],"being":[31],"analyzed.":[32],"Several":[33],"studies":[34],"found":[36],"interplay":[37],"of":[38,52,74,124,140],"imputation":[39,53,75,125],"methods":[40,43,56,126],"and":[41,48,54,62,77,86,103,107,127],"classification":[42,55,78,128],"respect":[45],"accuracy":[47,61,85,106],"fairness:":[49],"different":[50,60],"combinations":[51,102],"will":[57,92],"lead":[58],"fairness":[63,108],"results.":[64],"However,":[65],"it":[66,97],"unclear":[68],"how":[69],"choose":[71],"combination":[73,123],"method":[76,79,118,143,158],"optimize":[81],"tradeoff":[83],"between":[84],"fairness.":[87],"An":[88,130],"exhaustive":[89,145,156],"search":[90,157,163],"be":[93],"too":[94],"expensive":[95],"because":[96],"needs":[98],"check":[100],"all":[101],"measure":[104],"both":[105],"for":[109],"every":[110],"combination.":[111],"This":[112],"paper":[113],"proposes":[114],"beam-search":[116],"based":[117],"select":[120],"optimal":[122],"methods.":[129],"empirical":[131],"study":[132],"was":[133],"also":[134],"conducted":[135],"compare":[137],"performance":[139],"proposed":[142,148],"search.":[146],"The":[147],"solution":[149],"achieves":[150],"same":[152],"result":[153],"as":[154],"but":[159],"much":[161],"lower":[162],"cost.":[164]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}