{"id":"https://openalex.org/W4320712940","doi":"https://doi.org/10.1109/access.2023.3244682","title":"Semi-Supervised Gaussian Processes Active Learning Model for Imbalanced Small Data Based on Tri-Training With Data Enhancement","display_name":"Semi-Supervised Gaussian Processes Active Learning Model for Imbalanced Small Data Based on Tri-Training With Data Enhancement","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4320712940","doi":"https://doi.org/10.1109/access.2023.3244682"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3244682","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3244682","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10043703.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10043703.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011349409","display_name":"Chenxiao Zhou","orcid":"https://orcid.org/0000-0002-0157-0801"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenxiao Zhou","raw_affiliation_strings":["School of Electrical and Information, Wuhan Institute of Technology, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-0157-0801","affiliations":[{"raw_affiliation_string":"School of Electrical and Information, Wuhan Institute of Technology, Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034694495","display_name":"Lianying Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianying Zou","raw_affiliation_strings":["School of Electrical and Information, Wuhan Institute of Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information, Wuhan Institute of Technology, Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011349409"],"corresponding_institution_ids":["https://openalex.org/I91125648"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.5166,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.85418069,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"11","issue":null,"first_page":"17510","last_page":"17524"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9921000003814697,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9921000003814697,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.991599977016449,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9901000261306763,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7088524103164673},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6650473475456238},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5930473208427429},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5345370769500732},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5232890248298645},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.491074800491333},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4768039882183075},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4707273542881012},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4568418562412262},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.44842201471328735},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3301454782485962}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7088524103164673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6650473475456238},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5930473208427429},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5345370769500732},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5232890248298645},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.491074800491333},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4768039882183075},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4707273542881012},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4568418562412262},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.44842201471328735},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3301454782485962},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3244682","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3244682","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10043703.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:df87ea960205423abd019c33d990e540","is_oa":true,"landing_page_url":"https://doaj.org/article/df87ea960205423abd019c33d990e540","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":"IEEE Access, Vol 11, Pp 17510-17524 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3244682","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3244682","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10043703.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4320712940.pdf","grobid_xml":"https://content.openalex.org/works/W4320712940.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1970872889","https://openalex.org/W2048679005","https://openalex.org/W2133556223","https://openalex.org/W2148143831","https://openalex.org/W2295598076","https://openalex.org/W2511002675","https://openalex.org/W2911333381","https://openalex.org/W2913796112","https://openalex.org/W2914317721","https://openalex.org/W2943766732","https://openalex.org/W2976807053","https://openalex.org/W2999586291","https://openalex.org/W3013164371","https://openalex.org/W3034348890","https://openalex.org/W3036803654","https://openalex.org/W3041357421","https://openalex.org/W3044154489","https://openalex.org/W3080353943","https://openalex.org/W3086383900","https://openalex.org/W3108090463","https://openalex.org/W3128562015","https://openalex.org/W3133749727","https://openalex.org/W3138185948","https://openalex.org/W3138595103","https://openalex.org/W3193147073","https://openalex.org/W3203127079","https://openalex.org/W4291109999","https://openalex.org/W4292622358","https://openalex.org/W4304087050","https://openalex.org/W4306248002","https://openalex.org/W6758759751","https://openalex.org/W6768381829"],"related_works":["https://openalex.org/W2889302474","https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W4286910063","https://openalex.org/W2163707935","https://openalex.org/W83146503","https://openalex.org/W2169074127","https://openalex.org/W202723009","https://openalex.org/W2188612292","https://openalex.org/W2168489430"],"abstract_inverted_index":{"To":[0],"solve":[1],"the":[2,33,51,55,68,85,93,96,108,124,127,152,163,174,178,185,196,209,227],"problem":[3],"that":[4,107,221],"some":[5],"imbalanced":[6,236],"small":[7],"sample":[8],"datasets":[9,234],"only":[10],"contain":[11],"a":[12,16,43],"few":[13],"labeled":[14,97,202],"samples,":[15],"semi-supervised":[17,75],"gaussian":[18],"processes":[19],"active":[20,159,183,205],"learning":[21,76,160],"model":[22,210,229],"based":[23,49,78,161],"on":[24,50,79,162,232],"improved":[25,74],"tri-training":[26,80,112],"with":[27,132,223],"enhanced":[28,45,61,70],"data":[29,46,62,65],"is":[30,81,120,170,192],"proposed.":[31],"Firstly,":[32],"label":[34],"samples":[35,87,98,156,180,200,203],"are":[36],"balanced":[37],"and":[38,40,54,63,116,135,150,166,201,235],"enhanced,":[39],"we":[41],"present":[42],"quantitative":[44],"evaluation":[47],"criteria":[48],"JS":[52,167,190],"distance":[53,191],"similarity":[56,186,197],"of":[57,95,111,126,141,173,177,189,198],"information":[58],"entropy":[59],"between":[60],"original":[64],"to":[66,83,105,122,148,194],"select":[67],"best":[69],"data.":[71],"Secondly,":[72],"an":[73],"method":[77],"proposed":[82,228],"find":[84],"unlabeled":[86,155,179,199],"which":[88],"have":[89,113],"high":[90,175],"confidence,":[91],"so":[92,208],"certainty":[94],"group":[99],"can":[100,211],"be":[101],"increased,":[102],"in":[103,146,204],"order":[104,147],"ensure":[106],"three":[109,130],"classifiers":[110],"both":[114],"difference":[115],"robustness,":[117],"random":[118],"forest":[119],"introduced":[121,193],"divide":[123],"features":[125],"dataset":[128],"into":[129],"groups":[131],"equal":[133],"contribution,":[134],"each":[136],"classifier":[137],"trains":[138],"different":[139],"combinations":[140],"two":[142],"feature":[143],"groups.":[144],"Thirdly,":[145],"query":[149],"classify":[151,212],"most":[153],"informative":[154],"more":[157,213],"precisely,":[158],"Gaussian":[164],"process":[165],"distribution":[168,187],"range":[169,188],"structured,":[171],"because":[172],"uncertainty":[176],"predicted":[181],"by":[182],"learning,":[184],"compare":[195],"learning`s":[206],"classifier,":[207],"diverse":[214],"samples.":[215],"The":[216],"final":[217],"experimental":[218],"results":[219],"show":[220],"compared":[222],"several":[224],"traditional":[225],"models,":[226],"performs":[230],"better":[231],"artificial":[233],"small-size":[237],"UCI":[238],"datasets.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
