{"id":"https://openalex.org/W2884226717","doi":"https://doi.org/10.3233/jifs-169722","title":"Safe semi-supervised classification algorithm combined with active learning sampling strategy","display_name":"Safe semi-supervised classification algorithm combined with active learning sampling strategy","publication_year":2018,"publication_date":"2018-07-16","ids":{"openalex":"https://openalex.org/W2884226717","doi":"https://doi.org/10.3233/jifs-169722","mag":"2884226717"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-169722","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-169722","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-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/A5104080365","display_name":"Jianhua Zhao","orcid":"https://orcid.org/0000-0003-4028-8478"},"institutions":[{"id":"https://openalex.org/I4210101072","display_name":"Shangluo University","ror":"https://ror.org/01a56n213","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210101072"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianhua Zhao","raw_affiliation_strings":["College of Mathematics and Computer Application, Shangluo University, Shangluo, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Computer Application, Shangluo University, Shangluo, China","institution_ids":["https://openalex.org/I4210101072"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101683584","display_name":"Ning Liu","orcid":"https://orcid.org/0009-0008-5231-1835"},"institutions":[{"id":"https://openalex.org/I4210101072","display_name":"Shangluo University","ror":"https://ror.org/01a56n213","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210101072"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Liu","raw_affiliation_strings":["College of Economics Management, Shangluo University, Shangluo, China"],"affiliations":[{"raw_affiliation_string":"College of Economics Management, Shangluo University, Shangluo, China","institution_ids":["https://openalex.org/I4210101072"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080644778","display_name":"\u0410\u043b\u0435\u043a\u0441\u0430\u043d\u0434\u0440 \u041d\u0438\u043a\u043e\u043b\u0430\u0435\u0432\u0438\u0447 \u041c\u0430\u043b\u043e\u0432","orcid":null},"institutions":[{"id":"https://openalex.org/I55783418","display_name":"University of Life Sciences in Pozna\u0144","ror":"https://ror.org/03tth1e03","country_code":"PL","type":"education","lineage":["https://openalex.org/I55783418"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"A. Malov","raw_affiliation_strings":["Department of Mathematical and Statistical Methods, Poznan University Life Sci, Poznan, Poland"],"affiliations":[{"raw_affiliation_string":"Department of Mathematical and Statistical Methods, Poznan University Life Sci, Poznan, Poland","institution_ids":["https://openalex.org/I55783418"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5104080365"],"corresponding_institution_ids":["https://openalex.org/I4210101072"],"apc_list":null,"apc_paid":null,"fwci":0.4887,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.7262234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"35","issue":"4","first_page":"4001","last_page":"4010"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9980999827384949,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9980999827384949,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9751999974250793,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9621000289916992,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7496814727783203},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.679440438747406},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6002501249313354},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.571617841720581},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5430386662483215},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5368640422821045},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5191213488578796},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5045183897018433},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.49276211857795715},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.463158518075943},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.42861244082450867},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.41372859477996826},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17752456665039062}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7496814727783203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.679440438747406},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6002501249313354},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.571617841720581},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5430386662483215},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5368640422821045},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5191213488578796},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5045183897018433},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.49276211857795715},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.463158518075943},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.42861244082450867},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.41372859477996826},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17752456665039062},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-169722","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-169722","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1775107028","https://openalex.org/W1984671294","https://openalex.org/W1984989304","https://openalex.org/W1985767166","https://openalex.org/W1990334093","https://openalex.org/W2012844989","https://openalex.org/W2012878613","https://openalex.org/W2029604420","https://openalex.org/W2033505180","https://openalex.org/W2041453872","https://openalex.org/W2048679005","https://openalex.org/W2058224795","https://openalex.org/W2075604337","https://openalex.org/W2085443648","https://openalex.org/W2090093112","https://openalex.org/W2096623936","https://openalex.org/W2107131609","https://openalex.org/W2134284153","https://openalex.org/W2139709458","https://openalex.org/W2166451985","https://openalex.org/W2283626220","https://openalex.org/W2332761596","https://openalex.org/W2335919631","https://openalex.org/W2550196177","https://openalex.org/W2562586459","https://openalex.org/W2563619498","https://openalex.org/W2565437986","https://openalex.org/W2565531358","https://openalex.org/W2592184791","https://openalex.org/W2604674713","https://openalex.org/W2616803684","https://openalex.org/W2618885261","https://openalex.org/W2769333087","https://openalex.org/W2784625001","https://openalex.org/W2903158431","https://openalex.org/W4206723194","https://openalex.org/W4252861488","https://openalex.org/W4293171766","https://openalex.org/W4298132949","https://openalex.org/W6647676880","https://openalex.org/W6756615331"],"related_works":["https://openalex.org/W2949671220","https://openalex.org/W4312414840","https://openalex.org/W34092691","https://openalex.org/W2794908468","https://openalex.org/W2531570999","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W4206276646","https://openalex.org/W101422005","https://openalex.org/W192740413"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,46,93,152],"improve":[3],"the":[4,40,48,89,109,112,118,122,134,137,145,149,154,170,175,191,195],"performance":[5],"of":[6,11,63,103,121,128,148,156],"semi-supervised":[7,13,58,185],"learning,":[8],"a":[9,61,126,164],"kind":[10],"safe":[12],"classification":[14],"algorithm":[15,38],"based":[16,29,66],"active":[17,25,80],"learning":[18,26],"sampling":[19,27],"strategy":[20],"is":[21,34,44,70,73,86,115,131,142,167,172],"proposed.":[22],"First,":[23],"an":[24],"method":[28,62,127],"on":[30,67,75,184],"uncertainty":[31,41],"and":[32,42,54,98,108,187,190],"representativenes":[33],"designed.":[35,71,132],"The":[36,82,100,179],"weighted":[37],"combining":[39],"representativenesss":[43],"used":[45],"select":[47],"unlabeled":[49,76,123,150],"samples":[50],"with":[51,84],"rich":[52],"information":[53],"representation,":[55],"providing":[56],"for":[57],"learning.":[59,81],"Second,":[60],"label":[64,120,135,147],"prediction":[65],"grouping":[68],"verification":[69,130],"Prelabeling":[72],"executed":[74,162],"sample":[77,83,91,151],"selected":[78,116,143],"by":[79],"pseudo-label":[85,110],"added":[87],"into":[88],"labeled":[90,157,177],"set":[92],"carry":[94],"out":[95,183],"grouping,":[96],"training":[97],"testing.":[99],"corresponding":[101],"errors":[102],"various":[104],"pseudo-labels":[105],"are":[106,160,181,198],"calculated":[107],"making":[111,136],"accuracy":[113,138],"least":[114],"as":[117,144],"candidate":[119],"sample.":[124],"Third,":[125],"security":[129],"Only":[133],"lower":[139],"than":[140],"before":[141],"final":[146,176],"expand":[153],"number":[155],"samples.":[158],"Iterations":[159],"repeatedly":[161],"until":[163],"certain":[165],"precision":[166],"met.":[168],"Finally,":[169],"classifier":[171],"trained":[173],"using":[174],"set.":[178],"experiments":[180],"carried":[182],"datasets":[186],"UCI":[188],"datasets,":[189],"results":[192],"show":[193],"that":[194],"proposed":[196],"algorithms":[197],"effective.":[199]},"counts_by_year":[{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}