{"id":"https://openalex.org/W3116737767","doi":"https://doi.org/10.1109/ictc49870.2020.9289188","title":"Generative Adversarial Network for Robust Regression using Continuous Dataset","display_name":"Generative Adversarial Network for Robust Regression using Continuous Dataset","publication_year":2020,"publication_date":"2020-10-21","ids":{"openalex":"https://openalex.org/W3116737767","doi":"https://doi.org/10.1109/ictc49870.2020.9289188","mag":"3116737767"},"language":"en","primary_location":{"id":"doi:10.1109/ictc49870.2020.9289188","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc49870.2020.9289188","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","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/A5026213379","display_name":"Yu-Lim Min","orcid":null},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]},{"id":"https://openalex.org/I118692353","display_name":"University of Science and Technology","ror":"https://ror.org/05bj7sh33","country_code":"YE","type":"education","lineage":["https://openalex.org/I118692353"]}],"countries":["KR","YE"],"is_corresponding":true,"raw_author_name":"Yu-Lim Min","raw_affiliation_strings":["Electronics and Telecommunications Research Institute","University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute","institution_ids":["https://openalex.org/I142401562"]},{"raw_affiliation_string":"University of Science and Technology","institution_ids":["https://openalex.org/I118692353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074381877","display_name":"Seung-Jin Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I118692353","display_name":"University of Science and Technology","ror":"https://ror.org/05bj7sh33","country_code":"YE","type":"education","lineage":["https://openalex.org/I118692353"]},{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR","YE"],"is_corresponding":false,"raw_author_name":"Seung-Jin Hong","raw_affiliation_strings":["Electronics and Telecommunications Research Institute","University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute","institution_ids":["https://openalex.org/I142401562"]},{"raw_affiliation_string":"University of Science and Technology","institution_ids":["https://openalex.org/I118692353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381263","display_name":"Hye-Jin Kim","orcid":"https://orcid.org/0000-0003-3299-2932"},"institutions":[{"id":"https://openalex.org/I118692353","display_name":"University of Science and Technology","ror":"https://ror.org/05bj7sh33","country_code":"YE","type":"education","lineage":["https://openalex.org/I118692353"]},{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR","YE"],"is_corresponding":false,"raw_author_name":"Hye-jin Kim","raw_affiliation_strings":["Electronics and Telecommunications Research Institute","University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute","institution_ids":["https://openalex.org/I142401562"]},{"raw_affiliation_string":"University of Science and Technology","institution_ids":["https://openalex.org/I118692353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011760560","display_name":"Seung\u2010Ik Lee","orcid":"https://orcid.org/0000-0003-2986-7540"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]},{"id":"https://openalex.org/I118692353","display_name":"University of Science and Technology","ror":"https://ror.org/05bj7sh33","country_code":"YE","type":"education","lineage":["https://openalex.org/I118692353"]}],"countries":["KR","YE"],"is_corresponding":false,"raw_author_name":"Seung-Ik Lee","raw_affiliation_strings":["Electronics and Telecommunications Research Institute","University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute","institution_ids":["https://openalex.org/I142401562"]},{"raw_affiliation_string":"University of Science and Technology","institution_ids":["https://openalex.org/I118692353"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026213379"],"corresponding_institution_ids":["https://openalex.org/I118692353","https://openalex.org/I142401562"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57311008,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1209","last_page":"1211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9884999990463257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.8740990161895752},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7280296087265015},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6461066007614136},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6276280879974365},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5662705898284912},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5262210369110107},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4989900588989258},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.48935455083847046},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4825303256511688},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4730638265609741},{"id":"https://openalex.org/keywords/robust-regression","display_name":"Robust regression","score":0.46862509846687317},{"id":"https://openalex.org/keywords/nonlinear-regression","display_name":"Nonlinear regression","score":0.42797213792800903},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.423003613948822},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3781706690788269},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3304443955421448},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17539775371551514},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15760600566864014},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.062152713537216187}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8740990161895752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7280296087265015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6461066007614136},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6276280879974365},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5662705898284912},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5262210369110107},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4989900588989258},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.48935455083847046},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4825303256511688},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4730638265609741},{"id":"https://openalex.org/C70259352","wikidata":"https://www.wikidata.org/wiki/Q1847839","display_name":"Robust regression","level":3,"score":0.46862509846687317},{"id":"https://openalex.org/C46889948","wikidata":"https://www.wikidata.org/wiki/Q2755024","display_name":"Nonlinear regression","level":3,"score":0.42797213792800903},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.423003613948822},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3781706690788269},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3304443955421448},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17539775371551514},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15760600566864014},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.062152713537216187},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc49870.2020.9289188","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc49870.2020.9289188","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2099471712","https://openalex.org/W2548275288","https://openalex.org/W2950776302","https://openalex.org/W3034292309","https://openalex.org/W3120740533","https://openalex.org/W4320013936","https://openalex.org/W6729482032","https://openalex.org/W6776451990"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W2113881550","https://openalex.org/W4200298638","https://openalex.org/W1833314573","https://openalex.org/W4235679112","https://openalex.org/W2386035800","https://openalex.org/W2895656349","https://openalex.org/W3032356652"],"abstract_inverted_index":{"Recently,":[0],"advanced":[1],"neural":[2],"network,":[3],"which":[4],"is":[5,58],"implementing":[6],"technical":[7],"method,":[8],"has":[9],"focus":[10],"on":[11,90],"dealing":[12],"with":[13,107,125,164],"image":[14],"classification":[15,18],"problems.":[16],"Unlike":[17],"problems,":[19],"regression":[20,37,73,112],"provides":[21],"a":[22,84],"value":[23],"of":[24,77],"output":[25],"in":[26],"complex":[27],"and":[28,97,110,131,145,150],"sophisticated":[29],"continuous":[30,135],"datasets.":[31],"Though":[32],"nonlinear":[33,72],"models":[34,127],"can":[35],"perform":[36],"better":[38,103,169],"than":[39,171],"linear":[40],"model":[41,50,124,162],"as":[42,71,114,148],"Linear":[43,78,129],"Regression(LR),":[44],"the":[45,95,102,108,172],"difficulty":[46],"to":[47,59,74],"make":[48],"robust":[49,64],"still":[51],"remain.":[52],"In":[53,117,159],"this":[54],"paper,":[55],"our":[56,122,161],"purpose":[57],"design":[60],"training":[61],"architecture":[62,82,92],"for":[63],"regression.":[65],"We":[66,139],"approach":[67],"Neural":[68,87,132],"Network":[69,133],"known":[70],"solve":[75],"limitation":[76],"Regression.":[79],"Additionally,":[80],"Our":[81],"uses":[83],"new":[85],"artificial":[86],"Network(NN)":[88],"based":[89],"adversarial":[91],"by":[93,153],"using":[94,134,154],"Generator(G)":[96],"Discriminator(D).":[98],"The":[99],"Discriminator":[100],"shows":[101,168],"performance":[104,170],"while":[105],"competing":[106],"Generator":[109],"learning":[111],"problem":[113],"same":[115],"time.":[116],"evaluation":[118],"experiments,":[119],"we":[120],"compare":[121],"proposed":[123],"baseline":[126,173],"including":[128],"Regression":[130],"real":[136],"world":[137],"data.":[138],"split":[140],"four":[141],"datasets":[142],"into":[143],"train":[144],"test":[146],"sets":[147],"90:10":[149],"evaluate":[151],"them":[152],"Mean":[155],"Squared":[156],"Error(MSE)":[157],"function.":[158],"summary,":[160],"trained":[163],"Generative":[165],"Adversarial":[166],"Network(GAN)":[167],"models.":[174]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}