{"id":"https://openalex.org/W3097818167","doi":"https://doi.org/10.1109/icnsc48988.2020.9238069","title":"Semi-Supervised Incremental Three-Way Decision Using Convolutional Neural Network","display_name":"Semi-Supervised Incremental Three-Way Decision Using Convolutional Neural Network","publication_year":2020,"publication_date":"2020-10-30","ids":{"openalex":"https://openalex.org/W3097818167","doi":"https://doi.org/10.1109/icnsc48988.2020.9238069","mag":"3097818167"},"language":"en","primary_location":{"id":"doi:10.1109/icnsc48988.2020.9238069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc48988.2020.9238069","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 Networking, Sensing and Control (ICNSC)","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/A5050947521","display_name":"Yuwei Liang","orcid":"https://orcid.org/0000-0001-9324-4124"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuwei Liang","raw_affiliation_strings":["School of Management and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076835782","display_name":"Huaxiong Li","orcid":"https://orcid.org/0000-0003-0395-1525"},"institutions":[{"id":"https://openalex.org/I206777745","display_name":"Nanjing Audit University","ror":"https://ror.org/04zj2bd87","country_code":"CN","type":"education","lineage":["https://openalex.org/I206777745"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaxiong Li","raw_affiliation_strings":["School of Information Engineering, Nanjing Audit University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Nanjing Audit University, Nanjing, China","institution_ids":["https://openalex.org/I206777745"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017613139","display_name":"Bing Huang","orcid":"https://orcid.org/0000-0001-9058-078X"},"institutions":[{"id":"https://openalex.org/I206777745","display_name":"Nanjing Audit University","ror":"https://ror.org/04zj2bd87","country_code":"CN","type":"education","lineage":["https://openalex.org/I206777745"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Huang","raw_affiliation_strings":["School of Information Engineering, Nanjing Audit University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Nanjing Audit University, Nanjing, China","institution_ids":["https://openalex.org/I206777745"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019629751","display_name":"Zhuohuai Guan","orcid":"https://orcid.org/0000-0002-6931-4345"},"institutions":[{"id":"https://openalex.org/I4210166367","display_name":"Nanjing Institute of Agricultural Mechanization","ror":"https://ror.org/05xgaq910","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210127390","https://openalex.org/I4210151987","https://openalex.org/I4210166367"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuohuai Guan","raw_affiliation_strings":["Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural affairs, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural affairs, Nanjing, China","institution_ids":["https://openalex.org/I4210166367"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019594660","display_name":"Pei Yang","orcid":"https://orcid.org/0000-0001-8926-9695"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pei Yang","raw_affiliation_strings":["School of Management and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12609631,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9986000061035156,"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/T10057","display_name":"Face and Expression Recognition","score":0.9986000061035156,"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/T11448","display_name":"Face recognition and analysis","score":0.9940000176429749,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9939000010490417,"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/computer-science","display_name":"Computer science","score":0.7627935409545898},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7064384818077087},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6957060098648071},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.561956524848938},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5531241297721863},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48971131443977356},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4892749488353729},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43223869800567627},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42888081073760986},{"id":"https://openalex.org/keywords/optimal-decision","display_name":"Optimal decision","score":0.4215439260005951},{"id":"https://openalex.org/keywords/total-cost","display_name":"Total cost","score":0.41769081354141235},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.29873186349868774}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7627935409545898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7064384818077087},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6957060098648071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.561956524848938},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5531241297721863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48971131443977356},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4892749488353729},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43223869800567627},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42888081073760986},{"id":"https://openalex.org/C150325174","wikidata":"https://www.wikidata.org/wiki/Q4335500","display_name":"Optimal decision","level":3,"score":0.4215439260005951},{"id":"https://openalex.org/C182299520","wikidata":"https://www.wikidata.org/wiki/Q1289588","display_name":"Total cost","level":2,"score":0.41769081354141235},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.29873186349868774},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnsc48988.2020.9238069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc48988.2020.9238069","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 Networking, Sensing and Control (ICNSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.800000011920929,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1458205654","display_name":null,"funder_award_id":"2016YFD0702100,2018YFB1402600","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8456209387","display_name":null,"funder_award_id":"71671086,61876097","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W830076066","https://openalex.org/W1686810756","https://openalex.org/W2067145099","https://openalex.org/W2076434944","https://openalex.org/W2094174855","https://openalex.org/W2097117768","https://openalex.org/W2097287674","https://openalex.org/W2117539524","https://openalex.org/W2119191234","https://openalex.org/W2145287260","https://openalex.org/W2163605009","https://openalex.org/W2166338096","https://openalex.org/W2194775991","https://openalex.org/W2214725774","https://openalex.org/W2530816535","https://openalex.org/W2600072788","https://openalex.org/W2604641681","https://openalex.org/W2784166218","https://openalex.org/W2792234535","https://openalex.org/W2806842871","https://openalex.org/W2899562587","https://openalex.org/W2902605166","https://openalex.org/W2912681837","https://openalex.org/W2913485292","https://openalex.org/W2951970475","https://openalex.org/W2952229419","https://openalex.org/W2953490229","https://openalex.org/W2962835968","https://openalex.org/W2964050365","https://openalex.org/W2973351188","https://openalex.org/W2989585950","https://openalex.org/W2999912861","https://openalex.org/W3036721391","https://openalex.org/W6623329352","https://openalex.org/W6674914833","https://openalex.org/W6684191040","https://openalex.org/W6735860611","https://openalex.org/W6764051988"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W4399188509","https://openalex.org/W4312417841"],"abstract_inverted_index":{"This":[0],"paper":[1,249],"aims":[2],"to":[3,163,207,221,251],"develop":[4],"a":[5,106,148,157,238],"novel":[6],"cost-sensitive":[7,136],"face":[8,60,137,260],"recognition":[9,16,61,108,138],"framework,":[10,139],"which":[11,140],"can":[12,42,233],"gain":[13],"the":[14,19,49,58,64,67,75,79,85,89,115,129,165,169,176,180,191,195,199,203,209,212,218,229,253],"desirable":[15],"results":[17],"with":[18,211,236],"least":[20],"total":[21,192,196,214],"cost.":[22,167,193,215],"By":[23,73],"combining":[24],"two":[25,259],"recently":[26],"rising":[27],"techniques:":[28],"deep":[29,80],"convolutional":[30],"neural":[31],"networks":[32],"(CNNs)":[33],"and":[34,47,78,97,179,185],"sequential":[35,132],"three-way":[36],"decision":[37,51,122,159,166,225],"(3WD)":[38],"method,":[39],"our":[40,135,256],"framework":[41],"automatically":[43],"label":[44],"new":[45],"samples":[46],"incorporates":[48],"delayed":[50,158],"into":[52],"decision-making":[53,149,177],"process.":[54],"We":[55],"first":[56],"explore":[57],"semi-supervised":[59,145],"method":[62,130,257],"in":[63,134,175,258],"case":[65],"of":[66,69,84,118,131,144,182,241,247,255],"scarcity":[68],"labeled":[70,94,155,242],"training":[71,95,206],"data.":[72,243],"learning":[74,146],"class":[76],"estimation":[77],"convolution":[81],"feature":[82],"extraction":[83],"unlabeled":[86,98],"data":[87,96,99],"jointly,":[88],"CNN":[90],"trained":[91],"by":[92],"both":[93],"is":[100,172,188,250],"generated.":[101],"Then,":[102],"rather":[103],"than":[104],"getting":[105],"lower":[107],"error":[109],"rate,":[110],"we":[111,127],"focus":[112],"on":[113],"seeking":[114],"minimum":[116],"cost":[117,171,184,187,197],"misclassification":[119,183],"at":[120],"each":[121,142],"step.":[123,150],"For":[124],"this":[125,248],"purpose,":[126],"introduce":[128],"3WD":[133],"take":[141],"iteration":[143],"as":[147,190,198],"When":[151],"there":[152],"are":[153],"insufficient":[154],"samples,":[156],"will":[160],"be":[161,234],"adopted":[162],"reduce":[164],"Finally,":[168],"test":[170,186],"also":[173],"considered":[174],"process,":[178],"sum":[181],"taken":[189],"Using":[194],"objective":[200],"function,":[201],"optimizing":[202],"performance":[204],"indicators,":[205],"get":[208,222],"classifier":[210],"smallest":[213],"In":[216],"short,":[217],"model":[219],"strives":[220],"an":[223],"optimal":[224],"step,":[226],"so":[227],"that":[228],"reliable":[230],"identification":[231],"result":[232],"obtained":[235],"only":[237],"small":[239],"number":[240],"The":[244],"work":[245],"value":[246],"prove":[252],"effectiveness":[254],"datasets.":[261]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
