{"id":"https://openalex.org/W2911442613","doi":"https://doi.org/10.1109/la-cci.2018.8625247","title":"Method of pruning the hidden layer of the extreme learning machine based on correlation coefficient","display_name":"Method of pruning the hidden layer of the extreme learning machine based on correlation coefficient","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2911442613","doi":"https://doi.org/10.1109/la-cci.2018.8625247","mag":"2911442613"},"language":"en","primary_location":{"id":"doi:10.1109/la-cci.2018.8625247","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci.2018.8625247","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","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/A5074677650","display_name":"Paulo Vitor de Campos Souza","orcid":"https://orcid.org/0000-0002-7343-5844"},"institutions":[{"id":"https://openalex.org/I3018999404","display_name":"Centro Universit\u00e1rio de Belo Horizonte","ror":"https://ror.org/04m3xd186","country_code":"BR","type":"education","lineage":["https://openalex.org/I3018999404"]},{"id":"https://openalex.org/I4210136482","display_name":"Federal Center for Technological Education of Minas Gerais","ror":"https://ror.org/04ch49185","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210136482"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Paulo Vitor de Campos Souza","raw_affiliation_strings":["Information Governance Secretariat, CEFET-MG/UNI-BH, Belo Horizonte, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Governance Secretariat, CEFET-MG/UNI-BH, Belo Horizonte, Brazil","institution_ids":["https://openalex.org/I3018999404","https://openalex.org/I4210136482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069081466","display_name":"Vanessa Souza Ara\u00fajo","orcid":"https://orcid.org/0000-0002-9836-232X"},"institutions":[{"id":"https://openalex.org/I4210135669","display_name":"Centro Universit\u00e1rio Una","ror":"https://ror.org/04dcx0979","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210135669"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Vanessa Souza Araujo","raw_affiliation_strings":["Student of the Undergraduate, Faculdade Una de Betim, Betim, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Student of the Undergraduate, Faculdade Una de Betim, Betim, Brazil","institution_ids":["https://openalex.org/I4210135669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022634516","display_name":"Augusto J\u00fanio Guimar\u00e3es","orcid":"https://orcid.org/0000-0003-1314-3441"},"institutions":[{"id":"https://openalex.org/I4210135669","display_name":"Centro Universit\u00e1rio Una","ror":"https://ror.org/04dcx0979","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210135669"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Augusto Junio Guimaraes","raw_affiliation_strings":["Student of the Undergraduate, Faculdade Una de Betim, Betim, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Student of the Undergraduate, Faculdade Una de Betim, Betim, Brazil","institution_ids":["https://openalex.org/I4210135669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007915713","display_name":"Vincius Jonathan Silva Araujo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135669","display_name":"Centro Universit\u00e1rio Una","ror":"https://ror.org/04dcx0979","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210135669"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Vincius Jonathan Silva Araujo","raw_affiliation_strings":["Student of the Undergraduate, Faculdade Una de Betim, Betim, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Student of the Undergraduate, Faculdade Una de Betim, Betim, Brazil","institution_ids":["https://openalex.org/I4210135669"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085558252","display_name":"Thiago Silva Rezende","orcid":"https://orcid.org/0000-0003-0040-8156"},"institutions":[{"id":"https://openalex.org/I4210135669","display_name":"Centro Universit\u00e1rio Una","ror":"https://ror.org/04dcx0979","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210135669"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Thiago Silva Rezende","raw_affiliation_strings":["Student of the Undergraduate, Faculdade Una de Betim, Betim, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Student of the Undergraduate, Faculdade Una de Betim, Betim, Brazil","institution_ids":["https://openalex.org/I4210135669"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0137,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.83371728,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"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/T12676","display_name":"Machine Learning and ELM","score":1.0,"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/T10320","display_name":"Neural Networks and Applications","score":0.9891999959945679,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9794999957084656,"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/pruning","display_name":"Pruning","score":0.7414010763168335},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.6565088033676147},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.596733570098877},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5595502257347107},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5347477793693542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.525542676448822},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.4917047619819641},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3956827223300934},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3788968622684479},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24511414766311646},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22638219594955444},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.0913558304309845},{"id":"https://openalex.org/keywords/botany","display_name":"Botany","score":0.0542466938495636}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7414010763168335},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.6565088033676147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.596733570098877},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5595502257347107},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5347477793693542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.525542676448822},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.4917047619819641},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3956827223300934},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3788968622684479},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24511414766311646},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22638219594955444},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0913558304309845},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0542466938495636},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/la-cci.2018.8625247","is_oa":false,"landing_page_url":"https://doi.org/10.1109/la-cci.2018.8625247","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1986336378","https://openalex.org/W1990701202","https://openalex.org/W2014694459","https://openalex.org/W2019718627","https://openalex.org/W2019797988","https://openalex.org/W2036887984","https://openalex.org/W2045427344","https://openalex.org/W2111072639","https://openalex.org/W2130378394","https://openalex.org/W2156909104","https://openalex.org/W2165967751","https://openalex.org/W2962202409","https://openalex.org/W3120740533"],"related_works":["https://openalex.org/W2372194214","https://openalex.org/W3081640970","https://openalex.org/W4200439127","https://openalex.org/W2021013714","https://openalex.org/W2412250669","https://openalex.org/W2359856669","https://openalex.org/W2078603735","https://openalex.org/W1488551807","https://openalex.org/W2023979854","https://openalex.org/W2560331580"],"abstract_inverted_index":{"Extreme":[0],"learning":[1,31],"machines":[2],"are":[3,164],"very":[4],"efficient":[5,242],"tools":[6],"to":[7,17,23,76,97,102,115,147,169,181,198,227,237],"perform":[8,103,228],"data":[9],"classification,":[10],"besides":[11],"being":[12],"a":[13,73,84,99,217],"more":[14,113,188],"effective":[15],"solution":[16,208],"the":[18,21,30,44,49,78,91,95,109,117,124,127,152,159,170,173,200,207,211,223,229,234],"structures":[19,186],"using":[20,108,155,185],"backpropagation":[22],"determine":[24],"values":[25],"at":[26],"intermediate":[27],"layers":[28],"of":[29,46,87,111,140,154,175,231],"model.":[32],"One":[33],"problem":[34],"faced":[35],"by":[36,68,135],"ELM":[37,50,59,79,92,133,201,215],"is":[38,53,130],"over-fitting,":[39],"which":[40],"usually":[41],"happens":[42],"when":[43,184],"amount":[45],"neurons":[47,112,157,163],"in":[48,90,158,210],"hidden":[51,80,160],"layer":[52,81],"high.":[54],"Several":[55],"pruning":[56,243],"methods":[57],"for":[58,245],"have":[60,98],"been":[61],"proposed,":[62],"including":[63],"statistical":[64,194],"techniques":[65,137,197],"and":[66,119,143,177,195,221],"control":[67],"correlation.":[69],"This":[70],"article":[71,212],"provides":[72],"simple":[74],"way":[75],"remove":[77,199],"based":[82],"on":[83],"correlation":[85,136,196],"coefficient":[86],"elements":[88],"present":[89],"architecture,":[93],"allowing":[94,233],"model":[96],"simpler":[100,218],"topology":[101],"its":[104],"pattern":[105],"classification":[106,230],"activities":[107],"selection":[110],"able":[114],"predict":[116],"responses":[118],"contain":[120],"less":[121],"entropy":[122],"between":[123],"data.":[125],"In":[126],"end,":[128],"it":[129],"verified":[131],"that":[132,166,191,206],"pruned":[134],"maintained":[138],"results":[139,179],"training":[141,176],"accuracy":[142,174],"test":[144,178],"statistically":[145],"equivalent":[146,180],"ELM's":[148],"original":[149],"proposal,":[150],"with":[151,187,216,222],"advantage":[153],"fewer":[156],"layer,":[161],"where":[162],"selected":[165],"best":[167],"contribute":[168],"model,":[171],"maintains":[172],"those":[182],"obtained":[183],"neuron":[189],"models":[190],"also":[192],"use":[193],"architecture.":[202],"Statistical":[203],"tests":[204],"confirm":[205],"proposed":[209,235],"allows":[213],"an":[214,241],"internal":[219],"architecture":[220],"high":[224],"precision":[225],"capacity":[226],"standards,":[232],"methodology":[236],"be":[238],"seen":[239],"as":[240],"technique":[244],"ELM.":[246]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}