{"id":"https://openalex.org/W3139063704","doi":"https://doi.org/10.1109/bigdata50022.2020.9378240","title":"IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks","display_name":"IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3139063704","doi":"https://doi.org/10.1109/bigdata50022.2020.9378240","mag":"3139063704"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378240","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 Big Data (Big Data)","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/A5021807054","display_name":"Wei Qiu","orcid":"https://orcid.org/0000-0003-4030-9718"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Qiu","raw_affiliation_strings":["Yuanpei College, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Yuanpei College, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082426637","display_name":"Yangsibo Huang","orcid":"https://orcid.org/0000-0002-0640-4845"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangsibo Huang","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058429770","display_name":"Quanzheng Li","orcid":"https://orcid.org/0000-0002-9651-5820"},"institutions":[{"id":"https://openalex.org/I4210087915","display_name":"Massachusetts General Hospital","ror":"https://ror.org/002pd6e78","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210087915","https://openalex.org/I48633490"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Quanzheng Li","raw_affiliation_strings":["Department of Radiology, Massachusetts General Hospital, Boston, MA"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, Massachusetts General Hospital, Boston, MA","institution_ids":["https://openalex.org/I4210087915"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021807054"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.3908,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.63700679,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4715","last_page":"4723"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.988099992275238,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.8993301391601562},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.872389018535614},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.820106565952301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6989037394523621},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5385566353797913},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5195810794830322},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5164952278137207},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46473148465156555},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39292678236961365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3454875349998474}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8993301391601562},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.872389018535614},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.820106565952301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6989037394523621},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5385566353797913},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5195810794830322},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5164952278137207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46473148465156555},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39292678236961365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3454875349998474},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378240","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 Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W263545290","https://openalex.org/W1919216911","https://openalex.org/W1969496006","https://openalex.org/W1977177161","https://openalex.org/W2021404082","https://openalex.org/W2068127873","https://openalex.org/W2072604363","https://openalex.org/W2082907106","https://openalex.org/W2085988980","https://openalex.org/W2088422930","https://openalex.org/W2096863518","https://openalex.org/W2097998348","https://openalex.org/W2099471712","https://openalex.org/W2101234009","https://openalex.org/W2106761633","https://openalex.org/W2146130798","https://openalex.org/W2146332392","https://openalex.org/W2238387021","https://openalex.org/W2242719046","https://openalex.org/W2295598076","https://openalex.org/W2396881363","https://openalex.org/W2738588019","https://openalex.org/W2788592841","https://openalex.org/W2798365772","https://openalex.org/W2803403013","https://openalex.org/W2952935243","https://openalex.org/W2963258546","https://openalex.org/W2963420272","https://openalex.org/W3003365835","https://openalex.org/W3102476541","https://openalex.org/W4247777826","https://openalex.org/W4320013936","https://openalex.org/W6609866339","https://openalex.org/W6674385629","https://openalex.org/W6675354045","https://openalex.org/W6682042988","https://openalex.org/W6704369950","https://openalex.org/W6751145664"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Missing":[0],"value":[1,21],"imputation":[2,22],"is":[3,34,41,50,63],"a":[4,19,38,48],"challenging":[5],"and":[6,74],"well-":[7],"researched":[8],"topic":[9],"in":[10],"data":[11,68,70],"mining.":[12],"In":[13],"this":[14],"paper,":[15],"we":[16],"propose":[17],"IFGAN,":[18],"missing":[20,45,72,75,101],"algorithm":[23,98],"based":[24],"on":[25,89],"Feature-":[26],"specific":[27],"Generative":[28],"Adversarial":[29],"Networks":[30],"(GAN).":[31],"Our":[32],"idea":[33],"intuitive":[35],"yet":[36],"effective:":[37],"feature-specific":[39],"generator":[40],"trained":[42],"to":[43,52],"impute":[44],"values,":[46],"while":[47],"discriminator":[49],"expected":[51],"distinguish":[53],"the":[54],"imputed":[55],"values":[56],"from":[57],"observed":[58],"ones.":[59],"The":[60],"proposed":[61],"architecture":[62],"capable":[64],"of":[65],"handling":[66],"different":[67],"types,":[69],"distributions,":[71],"mechanisms,":[73],"rates.":[76],"It":[77],"also":[78],"improves":[79],"post-imputation":[80],"analysis":[81],"by":[82],"preserving":[83],"inter-feature":[84],"correlations.":[85],"We":[86],"empirically":[87],"show":[88],"several":[90],"real-life":[91],"datasets":[92],"that":[93],"IFGAN":[94],"outperforms":[95],"current":[96],"state-of-the-art":[97],"under":[99],"various":[100],"conditions.":[102]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
