{"id":"https://openalex.org/W7125893340","doi":"https://doi.org/10.1109/smc58881.2025.11343493","title":"VAEGAN-based architecture for missing values imputation in sparse datasets","display_name":"VAEGAN-based architecture for missing values imputation in sparse datasets","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125893340","doi":"https://doi.org/10.1109/smc58881.2025.11343493"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5124096078","display_name":"Yanis Charbti","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106624","display_name":"\u00c9cole Nationale d'Ing\u00e9nieurs de Gab\u00e8s","ror":"https://ror.org/01fy5k372","country_code":"TN","type":"education","lineage":["https://openalex.org/I4210106624"]},{"id":"https://openalex.org/I68916915","display_name":"University of Gab\u00e8s","ror":"https://ror.org/022efad20","country_code":"TN","type":"education","lineage":["https://openalex.org/I68916915"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Yanis Charbti","raw_affiliation_strings":["University of Gabes,National Engineering School of Gabès (ENIG),Tunisia"],"affiliations":[{"raw_affiliation_string":"University of Gabes,National Engineering School of Gabès (ENIG),Tunisia","institution_ids":["https://openalex.org/I4210106624","https://openalex.org/I68916915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066844912","display_name":"Hasna Njah","orcid":"https://orcid.org/0000-0002-0050-7173"},"institutions":[{"id":"https://openalex.org/I4210088638","display_name":"Higher Institute of Management","ror":"https://ror.org/003sxhp42","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210088638"]},{"id":"https://openalex.org/I68916915","display_name":"University of Gab\u00e8s","ror":"https://ror.org/022efad20","country_code":"TN","type":"education","lineage":["https://openalex.org/I68916915"]}],"countries":["RU","TN"],"is_corresponding":false,"raw_author_name":"Hasna Njah","raw_affiliation_strings":["University of Gabes,Higher Institute of Computer Sciences and Multimedia (ISIMG),Tunisia"],"affiliations":[{"raw_affiliation_string":"University of Gabes,Higher Institute of Computer Sciences and Multimedia (ISIMG),Tunisia","institution_ids":["https://openalex.org/I68916915","https://openalex.org/I4210088638"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5124096078"],"corresponding_institution_ids":["https://openalex.org/I4210106624","https://openalex.org/I68916915"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71501347,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3268","last_page":"3273"},"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":0.7354999780654907,"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":0.7354999780654907,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.0681999996304512,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.02759999968111515,"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/imputation","display_name":"Imputation (statistics)","score":0.7605999708175659},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7389000058174133},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6000999808311462},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5799000263214111},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4878000020980835},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.3977000117301941},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.35839998722076416},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.35420000553131104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3513000011444092}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7605999708175659},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7423999905586243},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7389000058174133},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6000999808311462},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5799000263214111},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5360000133514404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5080999732017517},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4878000020980835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45570001006126404},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.35839998722076416},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.32120001316070557},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6796314120292664,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W3038129359","https://openalex.org/W4319335604","https://openalex.org/W4386025626","https://openalex.org/W4394842437","https://openalex.org/W4395112008","https://openalex.org/W4396666499"],"related_works":[],"abstract_inverted_index":{"In":[0,81,103],"order":[1,82,104],"to":[2,83,105,173],"address":[3],"missing-value":[4],"prediction":[5],"in":[6,50,77,159,171,196],"sparse":[7,71],"datasets,":[8],"we":[9],"present":[10],"VAEGAIN,":[11],"a":[12,118,133,150,178],"novel":[13],"imputation":[14,75,101,108,142],"framework":[15,114],"modeled":[16],"after":[17],"transformer":[18],"architectures":[19],"that":[20,121],"combines":[21,122],"the":[22,31,51,113,187,197],"adversarial":[23,126],"robustness":[24],"of":[25,34],"Generative":[26],"Adversarial":[27],"Networks":[28],"(GANs)":[29],"with":[30,125],"representational":[32],"power":[33],"Variational":[35],"Autoencoders":[36],"(VAEs).":[37],"To":[38],"capture":[39],"intricate":[40],"feature":[41,64,111],"dependencies":[42],"and":[43,54,66,94,109,152,164],"maintain":[44],"statistical":[45],"coherence,":[46],"each":[47],"linear":[48],"transformation":[49],"GAN":[52],"discriminator":[53],"encoder\u2013decoder":[55],"pathways":[56],"is":[57,115],"parameterized.Because":[58],"high":[59],"missingness":[60],"can":[61],"skew":[62],"underlying":[63],"correlations":[65],"impair":[67],"downstream":[68],"model":[69],"performance,":[70],"data":[72,157],"poses":[73],"serious":[74],"challenges":[76],"many":[78],"real-world":[79],"scenarios.":[80],"overcome":[84],"these":[85],"problems,":[86],"VAEGAIN":[87,138,185],"uses":[88],"three":[89],"dynamically":[90],"generated":[91],"matrices\u2014data,":[92],"mask,":[93],"stochastic":[95],"noise\u2014to":[96],"direct":[97],"an":[98],"end-to-end,":[99],"context-aware":[100],"procedure.":[102],"ensure":[106],"accurate":[107],"strong":[110],"consistency,":[112],"optimized":[116],"using":[117],"composite":[119],"loss":[120],"reconstruction":[123,158],"error":[124],"objectives.An":[127],"experimental":[128],"evaluation":[129],"shows":[130],"that,":[131],"at":[132],"fixed":[134],"20%":[135],"missing":[136],"rate,":[137],"achieves":[139],"12-18%":[140],"higher":[141],"accuracy":[143],"than":[144],"state-of-the-art":[145],"baselines.":[146],"Our":[147],"approach":[148],"offers":[149],"trustworthy":[151,168],"interpretable":[153],"solution":[154],"for":[155,181,189],"incomplete":[156],"high-stakes":[160],"domains":[161],"like":[162],"healthcare":[163],"finance":[165],"by":[166],"providing":[167],"probabilistic":[169,190],"estimates":[170],"addition":[172],"imputed":[174],"values.":[175],"By":[176],"establishing":[177],"new":[179],"benchmark":[180],"hybrid,":[182],"uncertainty-aware":[183],"imputation,":[184],"opens":[186],"door":[188],"reasoning":[191],"on":[192],"sparse,":[193],"structured":[194],"datasets":[195],"future.":[198]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}