{"id":"https://openalex.org/W4415357771","doi":"https://doi.org/10.3390/a18100663","title":"Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder","display_name":"Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4415357771","doi":"https://doi.org/10.3390/a18100663"},"language":"en","primary_location":{"id":"doi:10.3390/a18100663","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18100663","pdf_url":"https://www.mdpi.com/1999-4893/18/10/663/pdf?version=1760869503","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1999-4893/18/10/663/pdf?version=1760869503","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027660188","display_name":"Georgios Kostopoulos","orcid":"https://orcid.org/0000-0002-7374-0099"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Georgios Kostopoulos","raw_affiliation_strings":["Department of Mathematics, University of Patras, 26504 Rion, Greece"],"raw_orcid":"https://orcid.org/0000-0002-7374-0099","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, 26504 Rion, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061070955","display_name":"Nikos Fazakis","orcid":"https://orcid.org/0000-0001-7687-2380"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Nikos Fazakis","raw_affiliation_strings":["Department of Mathematics, University of Patras, 26504 Rion, Greece"],"raw_orcid":"https://orcid.org/0000-0001-7687-2380","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, 26504 Rion, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066370772","display_name":"Sotiris Kotsiantis","orcid":"https://orcid.org/0000-0002-2247-3082"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Sotiris Kotsiantis","raw_affiliation_strings":["Department of Mathematics, University of Patras, 26504 Rion, Greece"],"raw_orcid":"https://orcid.org/0000-0002-2247-3082","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Patras, 26504 Rion, Greece","institution_ids":["https://openalex.org/I174878644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054871629","display_name":"Yannis Dimakopoulos","orcid":"https://orcid.org/0000-0002-8671-0657"},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Yiannis Dimakopoulos","raw_affiliation_strings":["Department of Chemical Engineering, University of Patras, 26504 Rion, Greece"],"raw_orcid":"https://orcid.org/0000-0002-8671-0657","affiliations":[{"raw_affiliation_string":"Department of Chemical Engineering, University of Patras, 26504 Rion, Greece","institution_ids":["https://openalex.org/I174878644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5027660188"],"corresponding_institution_ids":["https://openalex.org/I174878644"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":2.9133,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.9317627,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"18","issue":"10","first_page":"663","last_page":"663"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11122","display_name":"Online Learning and Analytics","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9904999732971191,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9750000238418579,"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/autoencoder","display_name":"Autoencoder","score":0.9208999872207642},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6366999745368958},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.6022999882698059},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.49309998750686646},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44670000672340393},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42989999055862427},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.39100000262260437},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3871999979019165}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9208999872207642},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7556999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6549999713897705},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6366999745368958},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.6022999882698059},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5604000091552734},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.49309998750686646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4772000014781952},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2946000099182129},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C2780365114","wikidata":"https://www.wikidata.org/wiki/Q169478","display_name":"MATLAB","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/a18100663","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18100663","pdf_url":"https://www.mdpi.com/1999-4893/18/10/663/pdf?version=1760869503","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8551ac1d69c64fb99d52e180e6984626","is_oa":true,"landing_page_url":"https://doaj.org/article/8551ac1d69c64fb99d52e180e6984626","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":"Algorithms, Vol 18, Iss 10, p 663 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/a18100663","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a18100663","pdf_url":"https://www.mdpi.com/1999-4893/18/10/663/pdf?version=1760869503","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8768259947","display_name":null,"funder_award_id":"6001593","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415357771.pdf","grobid_xml":"https://content.openalex.org/works/W4415357771.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1493009343","https://openalex.org/W1529758950","https://openalex.org/W1983320747","https://openalex.org/W1990334093","https://openalex.org/W2006444123","https://openalex.org/W2048679005","https://openalex.org/W2079359777","https://openalex.org/W2085443648","https://openalex.org/W2094490861","https://openalex.org/W2100960835","https://openalex.org/W2106401878","https://openalex.org/W2126514932","https://openalex.org/W2132466791","https://openalex.org/W2133556223","https://openalex.org/W2138334515","https://openalex.org/W2294104293","https://openalex.org/W2295598076","https://openalex.org/W2530395818","https://openalex.org/W2551441037","https://openalex.org/W2746791238","https://openalex.org/W2768348081","https://openalex.org/W2790999424","https://openalex.org/W2884834684","https://openalex.org/W2911964244","https://openalex.org/W2936653833","https://openalex.org/W2963917042","https://openalex.org/W2965229811","https://openalex.org/W2968914873","https://openalex.org/W2975317124","https://openalex.org/W3000065748","https://openalex.org/W3108671128","https://openalex.org/W3181414820","https://openalex.org/W3217265364","https://openalex.org/W4200027480","https://openalex.org/W4205656791","https://openalex.org/W4214922946","https://openalex.org/W4288296172","https://openalex.org/W4288359825","https://openalex.org/W4388532109","https://openalex.org/W4392726867"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"TVAE-SSL,":[3],"a":[4,41,91,126],"novel":[5],"semi-supervised":[6,164],"learning":[7,165],"(SSL)":[8],"paradigm":[9],"that":[10,131],"involves":[11],"Tabular":[12],"Variational":[13],"Autoencoder":[14],"(TVAE)-sampled":[15],"synthetic":[16,51,110],"data":[17,29,47,56,66],"injection":[18],"into":[19],"the":[20,44,54,70,98,103,139,154,161],"training":[21,40,80,114],"process":[22],"to":[23,48,76,159],"enhance":[24,160],"model":[25],"performance":[26],"under":[27],"low-label":[28],"conditions":[30],"in":[31,74,138,143],"Educational":[32],"Data":[33],"Mining":[34],"tasks.":[35],"The":[36],"algorithm":[37,85],"begins":[38],"with":[39,69,108],"TVAE":[42],"on":[43,97,125],"given":[45],"labeled":[46,141],"generate":[49],"imitative":[50],"samples":[52,60,107],"of":[53,105,128,145,156,163],"underlying":[55],"distribution.":[57],"These":[58],"synthesized":[59],"are":[61],"treated":[62],"as":[63],"additional":[64],"unlabeled":[65,72,106],"and":[67,117,148],"combined":[68,99],"original":[71],"ones":[73],"order":[75],"form":[77],"an":[78],"augmented":[79],"pool.":[81],"A":[82],"standard":[83],"SSL":[84],"(e.g.,":[86,94],"Self-Training)":[87],"is":[88],"trained":[89],"using":[90],"base":[92],"classifier":[93],"Random":[95],"Forest)":[96],"dataset.":[100],"By":[101],"expanding":[102],"pool":[104],"realistic":[109],"data,":[111],"TVAE-SSL":[112,132],"improves":[113],"sample":[115],"quantity":[116],"diversity":[118],"without":[119],"introducing":[120],"label":[121],"noise.":[122],"Large-scale":[123],"experiments":[124],"variety":[127],"datasets":[129],"demonstrate":[130,153],"can":[133],"outperform":[134],"baseline":[135],"supervised":[136],"models":[137],"full":[140],"dataset":[142],"terms":[144],"accuracy,":[146],"F1-score":[147],"fairness":[149],"metrics.":[150],"Our":[151],"results":[152],"capacity":[155],"generative":[157],"augmentation":[158],"effectiveness":[162],"for":[166],"tabular":[167],"data.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T08:27:34.040176","created_date":"2025-10-21T00:00:00"}
