{"id":"https://openalex.org/W2588024249","doi":"https://doi.org/10.1109/nafips.2016.7851595","title":"Performance evaluation of evolving classifier algorithms in high dimensional spaces","display_name":"Performance evaluation of evolving classifier algorithms in high dimensional spaces","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2588024249","doi":"https://doi.org/10.1109/nafips.2016.7851595","mag":"2588024249"},"language":"en","primary_location":{"id":"doi:10.1109/nafips.2016.7851595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nafips.2016.7851595","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","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/A5045756639","display_name":"Ranyeri Rocha","orcid":null},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Ranyeri Rocha","raw_affiliation_strings":["University of Campinas, Campinas, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"University of Campinas, Campinas, SP, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009222136","display_name":"Fernando Gomide","orcid":"https://orcid.org/0000-0001-5716-4282"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Fernando Gomide","raw_affiliation_strings":["University of Campinas, Campinas, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"University of Campinas, Campinas, SP, Brazil","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5045756639"],"corresponding_institution_ids":["https://openalex.org/I181391015"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.11126362,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"53","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9987000226974487,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9987000226974487,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.998199999332428,"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":0.9965999722480774,"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":0.7893586754798889},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7668371796607971},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6102246046066284},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5673917531967163},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.48660337924957275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4795784056186676},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4414181113243103},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4338434934616089},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39290085434913635}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7893586754798889},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7668371796607971},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6102246046066284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5673917531967163},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.48660337924957275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4795784056186676},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4414181113243103},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4338434934616089},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39290085434913635},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nafips.2016.7851595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nafips.2016.7851595","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W43637044","https://openalex.org/W322952782","https://openalex.org/W1530780135","https://openalex.org/W1594031697","https://openalex.org/W1972262648","https://openalex.org/W2014819080","https://openalex.org/W2022328110","https://openalex.org/W2035883480","https://openalex.org/W2052345845","https://openalex.org/W2069106362","https://openalex.org/W2090468668","https://openalex.org/W2105628133","https://openalex.org/W2119821739","https://openalex.org/W2135335717","https://openalex.org/W2147615893","https://openalex.org/W2153196467","https://openalex.org/W2156601771","https://openalex.org/W2170170977","https://openalex.org/W2171253128","https://openalex.org/W2330820318","https://openalex.org/W2341171179","https://openalex.org/W2913399920","https://openalex.org/W3003253354","https://openalex.org/W3120740533","https://openalex.org/W4239510810","https://openalex.org/W4244017338","https://openalex.org/W4248880341","https://openalex.org/W6680192438"],"related_works":["https://openalex.org/W4389000576","https://openalex.org/W2888412392","https://openalex.org/W2106570241","https://openalex.org/W2310485631","https://openalex.org/W1972053133","https://openalex.org/W2560076495","https://openalex.org/W2353098443","https://openalex.org/W2167825284","https://openalex.org/W2379474341","https://openalex.org/W2000311527"],"abstract_inverted_index":{"Evolving":[0],"systems":[1],"and":[2,14,28,36,38,64,119],"high":[3,33,68],"dimensional":[4,34,69],"stream":[5,70],"data":[6,35],"processing":[7,114],"algorithms":[8,41],"are":[9,16],"of":[10,43,47,98,102],"enormous":[11],"practical":[12],"importance":[13],"currently":[15],"under":[17],"intensive":[18],"investigation.":[19],"This":[20],"paper":[21],"introduces":[22],"an":[23],"evolving":[24,37,89,118],"neural":[25,61],"classifier":[26,40],"approach":[27,52,83,108],"evaluates":[29],"its":[30,65],"performance":[31],"using":[32,67],"classic":[39,120],"representative":[42],"the":[44,48,60,76,81,88,103,107,110,117],"current":[45],"state":[46],"art.":[49],"The":[50,72],"proposed":[51,82],"works":[53],"in":[54,92,100],"one-pass":[55],"mode":[56],"to":[57],"simultaneously":[58],"find":[59],"network":[62],"structure":[63],"weights":[66],"data.":[71],"results":[73],"suggests":[74],"that":[75],"classification":[77],"rate":[78],"achieved":[79],"by":[80],"is":[84],"very":[85],"competitive":[86],"with":[87],"models":[90],"addressed":[91],"this":[93],"paper.":[94],"It":[95],"outperforms":[96],"all":[97],"them":[99],"most":[101],"datasets":[104],"considered.":[105],"Also,":[106],"requires":[109],"lowest":[111],"per":[112],"sample":[113],"time":[115],"amongst":[116],"batch":[121],"classifiers.":[122]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}