{"id":"https://openalex.org/W2091152624","doi":"https://doi.org/10.1109/icmlc.2010.5581078","title":"2-Stage instance selection algorithm for KNN based on Nearest Unlike Neighbors","display_name":"2-Stage instance selection algorithm for KNN based on Nearest Unlike Neighbors","publication_year":2010,"publication_date":"2010-07-01","ids":{"openalex":"https://openalex.org/W2091152624","doi":"https://doi.org/10.1109/icmlc.2010.5581078","mag":"2091152624"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2010.5581078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2010.5581078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 International Conference on Machine Learning and Cybernetics","raw_type":"proceedings-article"},"type":"conference-paper","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/A5018560081","display_name":"Chun-Ru Dong","orcid":"https://orcid.org/0000-0003-1726-5534"},"institutions":[{"id":"https://openalex.org/I43337087","display_name":"Hebei University","ror":"https://ror.org/01p884a79","country_code":"CN","type":"education","lineage":["https://openalex.org/I43337087"]},{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chun-Ru Dong","raw_affiliation_strings":["Department of Mathematics and Computer Science, Hebei University, Baoding, China","Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Computer Science, Hebei University, Baoding, China","institution_ids":["https://openalex.org/I43337087"]},{"raw_affiliation_string":"Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031854980","display_name":"Patrick P. K. Chan","orcid":"https://orcid.org/0000-0001-7774-580X"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Patrick P. K. Chan","raw_affiliation_strings":["Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105759393","display_name":"Wing W. Y. Ng","orcid":"https://orcid.org/0000-0003-0783-3585"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wing W. Y. Ng","raw_affiliation_strings":["Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006543313","display_name":"Daniel Yeung","orcid":"https://orcid.org/0000-0001-9397-8865"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daniel S. Yeung","raw_affiliation_strings":["Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.20285024,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"6","issue":null,"first_page":"134","last_page":"140"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","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/T12535","display_name":"Machine Learning and Data Classification","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/T10057","display_name":"Face and Expression Recognition","score":0.9721999764442444,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9538000226020813,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7399939894676208},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.7215321063995361},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.660552442073822},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5884069204330444},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5601774454116821},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5182256698608398},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4862767457962036},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.46707987785339355},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4657839834690094},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.4518255591392517},{"id":"https://openalex.org/keywords/large-margin-nearest-neighbor","display_name":"Large margin nearest neighbor","score":0.4446973502635956},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4418213963508606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4120222330093384},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40925079584121704},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16839340329170227}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7399939894676208},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.7215321063995361},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.660552442073822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5884069204330444},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5601774454116821},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5182256698608398},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4862767457962036},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.46707987785339355},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4657839834690094},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.4518255591392517},{"id":"https://openalex.org/C94475309","wikidata":"https://www.wikidata.org/wiki/Q6489154","display_name":"Large margin nearest neighbor","level":3,"score":0.4446973502635956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4418213963508606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4120222330093384},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40925079584121704},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16839340329170227},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc.2010.5581078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2010.5581078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 International Conference on Machine Learning and Cybernetics","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":22,"referenced_works":["https://openalex.org/W100531594","https://openalex.org/W1610836425","https://openalex.org/W1984047604","https://openalex.org/W1994410331","https://openalex.org/W2056543212","https://openalex.org/W2067021215","https://openalex.org/W2097373217","https://openalex.org/W2098631313","https://openalex.org/W2105306795","https://openalex.org/W2107686700","https://openalex.org/W2122111042","https://openalex.org/W2122496402","https://openalex.org/W2155776210","https://openalex.org/W2160601232","https://openalex.org/W2162583212","https://openalex.org/W2799061466","https://openalex.org/W4235456164","https://openalex.org/W4244494905","https://openalex.org/W4249322402","https://openalex.org/W6636497728","https://openalex.org/W6674734074","https://openalex.org/W7014191107"],"related_works":["https://openalex.org/W2351157934","https://openalex.org/W2519241726","https://openalex.org/W2015442739","https://openalex.org/W1985839407","https://openalex.org/W2108882154","https://openalex.org/W2107754327","https://openalex.org/W2125687350","https://openalex.org/W2890061582","https://openalex.org/W2165396616","https://openalex.org/W2786437509"],"abstract_inverted_index":{"For":[0],"the":[1,13,27,69,77,90,101,111,114],"virtues":[2],"such":[3],"as":[4],"simplicity,":[5],"high":[6],"generalization":[7],"capability,":[8],"and":[9,23,73],"few":[10],"training":[11,58,79,105],"cost,":[12],"K-Nearest-Neighbor":[14],"(KNN)":[15],"classifier":[16,32],"is":[17,96],"widely":[18],"used":[19],"in":[20,104],"pattern":[21],"recognition":[22],"machine":[24],"learning.":[25],"However,":[26],"computation":[28],"complexity":[29],"of":[30,113],"KNN":[31,64],"will":[33,48],"become":[34],"higher":[35],"when":[36],"dealing":[37],"with":[38],"large":[39],"data":[40,71],"sets":[41],"classification":[42],"problem.":[43],"In":[44,107],"consequence,":[45],"its":[46],"efficiency":[47],"be":[49],"decreased":[50],"greatly.":[51],"This":[52],"paper":[53],"proposes":[54],"a":[55,82],"general":[56,63,83],"two-stage":[57],"set":[59],"condensing":[60],"algorithm":[61],"for":[62],"classifier.":[65],"First,":[66],"we":[67],"identify":[68],"noise":[70],"points":[72],"remove":[74],"them":[75],"from":[76],"original":[78],"set.":[80,106],"Second,":[81],"condensed":[84],"nearest":[85],"neighbor":[86],"rule":[87],"based":[88],"on":[89,122],"so-called":[91],"Nearest":[92],"Unlike":[93],"Neighbor":[94],"(NUN)":[95],"presented":[97],"to":[98,109],"further":[99],"eliminate":[100],"redundant":[102],"samples":[103],"order":[108],"verify":[110],"performance":[112],"proposed":[115],"method,":[116],"some":[117],"numerical":[118],"experiments":[119],"are":[120],"conducted":[121],"several":[123],"UCI":[124],"benchmark":[125],"databases.":[126]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
