{"id":"https://openalex.org/W3088412273","doi":"https://doi.org/10.1109/tvt.2020.3025739","title":"Error-Tolerant Computation for Voting Classifiers With Multiple Classes","display_name":"Error-Tolerant Computation for Voting Classifiers With Multiple Classes","publication_year":2020,"publication_date":"2020-09-21","ids":{"openalex":"https://openalex.org/W3088412273","doi":"https://doi.org/10.1109/tvt.2020.3025739","mag":"3088412273"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2020.3025739","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2020.3025739","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/10016/31890","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100417119","display_name":"Shanshan Liu","orcid":"https://orcid.org/0000-0001-6226-2880"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shanshan Liu","raw_affiliation_strings":["Department of ECE, Northeastern University, Boston, MA, USA","Dept of ECE, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Department of ECE, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]},{"raw_affiliation_string":"Dept of ECE, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080322790","display_name":"Pedro Reviriego","orcid":"https://orcid.org/0000-0003-2540-5234"},"institutions":[{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Pedro Reviriego","raw_affiliation_strings":["Universidad Carlos III de Madrid, Av. De la Universidad 30, Legan\u00e9s, Spain"],"affiliations":[{"raw_affiliation_string":"Universidad Carlos III de Madrid, Av. De la Universidad 30, Legan\u00e9s, Spain","institution_ids":["https://openalex.org/I50357001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083219829","display_name":"Paolo Montuschi","orcid":"https://orcid.org/0000-0003-2563-2250"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Montuschi","raw_affiliation_strings":["Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy","[Dipartimento di Automatica e Informatica, Politecnico Di Torino, Torino, Italy]"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy","institution_ids":["https://openalex.org/I177477856"]},{"raw_affiliation_string":"[Dipartimento di Automatica e Informatica, Politecnico Di Torino, Torino, Italy]","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001979328","display_name":"Fabrizio Lombardi","orcid":"https://orcid.org/0000-0003-3152-3245"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fabrizio Lombardi","raw_affiliation_strings":["Department of ECE, Northeastern University, Boston, MA, USA","Dept of ECE, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Department of ECE, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]},{"raw_affiliation_string":"Dept of ECE, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100417119"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":0.6857,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.76938698,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"69","issue":"11","first_page":"13718","last_page":"13727"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994999766349792,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994999766349792,"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/T11005","display_name":"Radiation Effects in Electronics","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12423","display_name":"Software Reliability and Analysis Research","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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/random-subspace-method","display_name":"Random subspace method","score":0.7846832275390625},{"id":"https://openalex.org/keywords/cascading-classifiers","display_name":"Cascading classifiers","score":0.6820253133773804},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6029037237167358},{"id":"https://openalex.org/keywords/weighted-voting","display_name":"Weighted voting","score":0.5921586155891418},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5796146392822266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5760876536369324},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5595765709877014},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5558192729949951},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.5356884002685547},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4962051510810852},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46095848083496094},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.4490641951560974},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4390985071659088},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43812116980552673},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3559364676475525},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.27052465081214905}],"concepts":[{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.7846832275390625},{"id":"https://openalex.org/C40651066","wikidata":"https://www.wikidata.org/wiki/Q5048220","display_name":"Cascading classifiers","level":4,"score":0.6820253133773804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6029037237167358},{"id":"https://openalex.org/C132778050","wikidata":"https://www.wikidata.org/wiki/Q2065430","display_name":"Weighted voting","level":4,"score":0.5921586155891418},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5796146392822266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5760876536369324},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5595765709877014},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5558192729949951},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.5356884002685547},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4962051510810852},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46095848083496094},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4490641951560974},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4390985071659088},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43812116980552673},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3559364676475525},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27052465081214905},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tvt.2020.3025739","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2020.3025739","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"},{"id":"pmh:oai:dnet:earchivo____::c783c295d34f3241ad228b969ad23df5","is_oa":true,"landing_page_url":"http://hdl.handle.net/10016/31890","pdf_url":"http://hdl.handle.net/10016/31890","source":{"id":"https://openalex.org/S4306402641","display_name":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Cient\u00edficas)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4383465926","host_organization_name":"LA Referencia","host_organization_lineage":["https://openalex.org/I4383465926"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:dnet:earchivo____::c783c295d34f3241ad228b969ad23df5","is_oa":true,"landing_page_url":"http://hdl.handle.net/10016/31890","pdf_url":"http://hdl.handle.net/10016/31890","source":{"id":"https://openalex.org/S4306402641","display_name":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Cient\u00edficas)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4383465926","host_organization_name":"LA Referencia","host_organization_lineage":["https://openalex.org/I4383465926"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3088412273.pdf"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W273955616","https://openalex.org/W429766147","https://openalex.org/W611078108","https://openalex.org/W1532264762","https://openalex.org/W1604956938","https://openalex.org/W1915261739","https://openalex.org/W2000379092","https://openalex.org/W2026131661","https://openalex.org/W2075424814","https://openalex.org/W2086685817","https://openalex.org/W2096927458","https://openalex.org/W2103459159","https://openalex.org/W2116079122","https://openalex.org/W2166494015","https://openalex.org/W2330876360","https://openalex.org/W2342408547","https://openalex.org/W2495920728","https://openalex.org/W2563468918","https://openalex.org/W2563686712","https://openalex.org/W2576845151","https://openalex.org/W2606436201","https://openalex.org/W2753941074","https://openalex.org/W2775916869","https://openalex.org/W2900028034","https://openalex.org/W2903732333","https://openalex.org/W2911964244","https://openalex.org/W2912707296","https://openalex.org/W2917452308","https://openalex.org/W2979997028","https://openalex.org/W2987656958","https://openalex.org/W3008809756","https://openalex.org/W3103169341","https://openalex.org/W3120740533","https://openalex.org/W6610017368","https://openalex.org/W6631721778"],"related_works":["https://openalex.org/W2137567908","https://openalex.org/W1940098242","https://openalex.org/W2534614934","https://openalex.org/W2913388591","https://openalex.org/W2083259201","https://openalex.org/W2142995730","https://openalex.org/W88722211","https://openalex.org/W2557740646","https://openalex.org/W2989868186","https://openalex.org/W1971238016"],"abstract_inverted_index":{"In":[0,83,150,250,277],"supervised":[1],"learning,":[2],"labeled":[3],"data":[4],"are":[5,27,61,71,191],"provided":[6],"as":[7,46,55,161,163,259,266],"inputs":[8],"and":[9,182,223],"then":[10],"learning":[11],"is":[12,38,156,177,187,244,264],"used":[13,34,73],"to":[14,45,54,77,127,158,193,236,258,270],"classify":[15],"new":[16,254,279],"observations.":[17],"Error":[18],"tolerance":[19],"should":[20],"be":[21,89,285],"guaranteed":[22],"for":[23,74,99,103,141,217,228,242,247],"classifiers":[24,37,60,146,166],"when":[25,234],"they":[26],"employed":[28],"in":[29,230,274],"critical":[30],"applications.":[31],"A":[32],"widely":[33],"type":[35],"of":[36,80,94,132,172,291,297],"based":[39],"on":[40,63],"voting":[41,165],"among":[42],"instances":[43],"(referred":[44,53],"single":[47,86,179],"voter":[48,180],"classifiers)":[49],"or":[50,101],"multiple":[51,159,199,237],"voters":[52],"ensemble":[56,189],"classifiers).":[57],"When":[58],"the":[59,78,81,92,111,129,153,169,173,195,201,205,210,239,278,281,289,302],"implemented":[62],"a":[64,178,253,293],"processor,":[65],"Time-Based":[66],"Modular":[67],"Redundancy":[68],"(TBMR)":[69],"techniques":[70],"often":[72],"protection":[75,255,273],"due":[76],"inflexibility":[79],"hardware.":[82],"TBMR,":[84,133],"any":[85],"error":[87],"can":[88,284,300],"handled":[90],"at":[91,115,288],"cost":[93,290],"additional":[95],"computing":[96],"either":[97],"once":[98],"detection":[100],"twice":[102],"correction":[104],"after":[105],"detection;":[106],"however,":[107],"this":[108,135,151,251],"technique":[109],"increases":[110],"computation":[112,130,211,282],"overhead":[113,131,212,283],"by":[114,167,213,224],"least":[116],"100%.":[117],"The":[118],"Voting":[119],"Margin":[120],"(VM)":[121],"scheme":[122,136,155,207,241,256],"has":[123,137],"recently":[124],"been":[125,139],"proposed":[126,196,206],"reduce":[128],"but":[134],"only":[138],"utilized":[140],"k":[142,260],"Nearest":[143],"Neighbors":[144],"(kNNs)":[145],"with":[147,219],"two":[148],"classes.":[149],"paper,":[152,252],"VM":[154,240],"extended":[157,235],"classes,":[160,238],"well":[162],"other":[164],"exploiting":[168],"intrinsic":[170],"robustness":[171],"algorithms.":[174],"kNNs":[175,218,243],"(that":[176,186],"classifier)":[181,190],"Random":[183],"Forest":[184],"(RF)":[185],"an":[188,267],"considered":[192],"evaluate":[194],"scheme.":[197],"Using":[198],"datasets,":[200],"results":[202],"show":[203],"that":[204,299],"significantly":[208],"reduces":[209],"more":[214,225],"than":[215,226],"70%":[216],"good":[220],"classification":[221,303],"accuracy":[222],"90%":[227],"RF":[229],"all":[231],"cases.":[232],"However,":[233],"not":[245],"efficient":[246,272],"some":[248],"datasets.":[249],"referred":[257],"+":[261],"1":[262],"NNs":[263],"presented":[265],"alternative":[268],"option":[269],"provide":[271],"those":[275],"scenarios.":[276],"scheme,":[280],"further":[286],"reduced":[287],"allowing":[292],"very":[294],"low":[295],"percentage":[296],"errors":[298],"modify":[301],"outcome.":[304]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-17T17:19:04.345684","created_date":"2025-10-10T00:00:00"}