{"id":"https://openalex.org/W2945914244","doi":"https://doi.org/10.1145/3318299.3318339","title":"Minimizing the Misclassification Rate of the Nearest Neighbor Rule Using a Two-stage Method","display_name":"Minimizing the Misclassification Rate of the Nearest Neighbor Rule Using a Two-stage Method","publication_year":2019,"publication_date":"2019-02-22","ids":{"openalex":"https://openalex.org/W2945914244","doi":"https://doi.org/10.1145/3318299.3318339","mag":"2945914244"},"language":"en","primary_location":{"id":"doi:10.1145/3318299.3318339","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3318299.3318339","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 11th International Conference on Machine Learning and Computing","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/A5055813170","display_name":"Yunlong Gao","orcid":"https://orcid.org/0000-0003-2843-9878"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunlong Gao","raw_affiliation_strings":["Department of Automation, Xiamen University, Fujian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081616294","display_name":"Sizhe Luo","orcid":"https://orcid.org/0000-0002-8334-3935"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sizhe Luo","raw_affiliation_strings":["Department of Automation, Xiamen University, Fujian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109953585","display_name":"Jinyan Pan","orcid":"https://orcid.org/0000-0003-2843-9878"},"institutions":[{"id":"https://openalex.org/I161346416","display_name":"Jimei University","ror":"https://ror.org/03hknyb50","country_code":"CN","type":"education","lineage":["https://openalex.org/I161346416"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyan Pan","raw_affiliation_strings":["Department of Information Engineering, Jimei University, Fujian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, Jimei University, Fujian, China","institution_ids":["https://openalex.org/I161346416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102011602","display_name":"Baihua Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baihua Chen","raw_affiliation_strings":["Department of Automation, Xiamen University, Fujian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen University, Fujian, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106667221","display_name":"Peng Gao","orcid":"https://orcid.org/0009-0005-7881-712X"},"institutions":[{"id":"https://openalex.org/I161346416","display_name":"Jimei University","ror":"https://ror.org/03hknyb50","country_code":"CN","type":"education","lineage":["https://openalex.org/I161346416"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Gao","raw_affiliation_strings":["Department of Information, Engineering, Jimei University, Fujian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information, Engineering, Jimei University, Fujian, China","institution_ids":["https://openalex.org/I161346416"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04349324,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"124","last_page":"132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9966999888420105,"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":0.9966999888420105,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9905999898910522,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.989799976348877,"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/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.8150180578231812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6537870764732361},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5864777565002441},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5864295959472656},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.498685359954834},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4643571376800537},{"id":"https://openalex.org/keywords/nearest-neighbour","display_name":"Nearest neighbour","score":0.4510844647884369},{"id":"https://openalex.org/keywords/best-bin-first","display_name":"Best bin first","score":0.4473339319229126},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.44704559445381165},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4348353147506714},{"id":"https://openalex.org/keywords/nearest-neighbor-chain-algorithm","display_name":"Nearest-neighbor chain algorithm","score":0.4233844578266144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2832142114639282},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.16000935435295105}],"concepts":[{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.8150180578231812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6537870764732361},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5864777565002441},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5864295959472656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.498685359954834},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4643571376800537},{"id":"https://openalex.org/C2983946233","wikidata":"https://www.wikidata.org/wiki/Q4088109","display_name":"Nearest neighbour","level":2,"score":0.4510844647884369},{"id":"https://openalex.org/C161986146","wikidata":"https://www.wikidata.org/wiki/Q4896845","display_name":"Best bin first","level":3,"score":0.4473339319229126},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.44704559445381165},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4348353147506714},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.4233844578266144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2832142114639282},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.16000935435295105},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3318299.3318339","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3318299.3318339","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 11th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1989617207","https://openalex.org/W2054498688","https://openalex.org/W2068514419","https://openalex.org/W2088779313","https://openalex.org/W2091632079","https://openalex.org/W2097757574","https://openalex.org/W2106053110","https://openalex.org/W2112827843","https://openalex.org/W2116596942","https://openalex.org/W2118522967","https://openalex.org/W2122111042","https://openalex.org/W2128629203","https://openalex.org/W2129578806","https://openalex.org/W2137452788","https://openalex.org/W2138181354","https://openalex.org/W2142339769","https://openalex.org/W2144935315","https://openalex.org/W2149992809","https://openalex.org/W2150470820","https://openalex.org/W2151537585","https://openalex.org/W2153325973","https://openalex.org/W2158320292","https://openalex.org/W2164031334","https://openalex.org/W2799061466"],"related_works":["https://openalex.org/W2375128115","https://openalex.org/W2519241726","https://openalex.org/W2182477562","https://openalex.org/W1671890395","https://openalex.org/W4297819076","https://openalex.org/W2351157934","https://openalex.org/W325985789","https://openalex.org/W2125687350","https://openalex.org/W2062957446","https://openalex.org/W2012496296"],"abstract_inverted_index":{"The":[0],"kNN":[1,113],"classification":[2,100],"performance":[3],"entirely":[4],"depends":[5],"on":[6,20,131],"the":[7,11,45,48,61,89,92,99,121,125,136],"selected":[8],"neighbors.":[9],"In":[10,54],"past,":[12],"many":[13],"nearest":[14,126],"neighbor":[15,97,127],"(NN)-based":[16],"methods":[17],"mainly":[18],"focus":[19],"learning":[21],"distance":[22,63,76,111],"measure":[23],"metrics":[24],"so":[25,102],"that":[26,103,135],"a":[27,73,104],"neighborhood":[28],"of":[29,47,51,91,95,124],"an":[30],"approximately":[31],"constant":[32],"posteriori":[33],"probability":[34],"can":[35,139],"be":[36],"produced,":[37],"whereas":[38],"limited":[39],"works":[40],"are":[41],"performed":[42],"to":[43,68,81,119],"study":[44],"influences":[46,90],"distribution":[49,93],"characteristics":[50,94],"each":[52,96],"neighbor.":[53],"this":[55,83],"paper,":[56],"we":[57,86],"point":[58],"out":[59],"why":[60],"best":[62,75,110],"measurement":[64,77,112],"(BDM)":[65],"is":[66,79,116],"sensitive":[67],"malicious":[69],"samples,":[70],"and":[71],"then":[72],"robust":[74,109],"(RBDM)":[78],"suggested":[80],"solve":[82],"problem.":[84],"Moreover,":[85],"also":[87],"investigated":[88],"for":[98],"performance,":[101],"two-stage":[105],"method,":[106],"called":[107],"weighted":[108],"method":[114,138],"(WRBDMkNN),":[115],"proposed":[117,137],"aiming":[118],"minimize":[120],"misclassification":[122],"rate":[123],"rule.":[128],"Extensive":[129],"experiments":[130],"diversity":[132],"datasets":[133],"indicate":[134],"achieve":[140],"more":[141],"encouraging":[142],"results":[143],"compared":[144],"with":[145],"some":[146],"state-of-the-art":[147],"NN-based":[148],"methods.":[149]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
