{"id":"https://openalex.org/W2977446031","doi":"https://doi.org/10.3233/ida-184190","title":"Adaptive geometric median prototype selection method for k-nearest neighbors classification","display_name":"Adaptive geometric median prototype selection method for k-nearest neighbors classification","publication_year":2019,"publication_date":"2019-09-26","ids":{"openalex":"https://openalex.org/W2977446031","doi":"https://doi.org/10.3233/ida-184190","mag":"2977446031"},"language":"en","primary_location":{"id":"doi:10.3233/ida-184190","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-184190","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-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/A5050888715","display_name":"Chatchai Kasemtaweechok","orcid":"https://orcid.org/0000-0003-0726-0465"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chatchai Kasemtaweechok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5089546239","display_name":"Worasait Suwannik","orcid":"https://orcid.org/0000-0002-1063-9532"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Worasait Suwannik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5050888715"],"corresponding_institution_ids":[],"apc_list":{"value":4150,"currency":"USD","value_usd":4150},"apc_paid":null,"fwci":0.3185,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70298874,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"23","issue":"4","first_page":"855","last_page":"876"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998999834060669,"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":0.9998999834060669,"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":0.996999979019165,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9958999752998352,"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":0.6753137111663818},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6528600454330444},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.6523791551589966},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.49229729175567627},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4876917600631714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48306161165237427},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48171859979629517},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48094457387924194},{"id":"https://openalex.org/keywords/selection-algorithm","display_name":"Selection algorithm","score":0.46320047974586487},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4265826344490051},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12249311804771423},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0675334632396698}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6753137111663818},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6528600454330444},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.6523791551589966},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.49229729175567627},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4876917600631714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48306161165237427},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48171859979629517},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48094457387924194},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.46320047974586487},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4265826344490051},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12249311804771423},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0675334632396698},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ida-184190","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-184190","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1985258161","https://openalex.org/W1994410331","https://openalex.org/W1994550352","https://openalex.org/W2011762057","https://openalex.org/W2024060531","https://openalex.org/W2027402472","https://openalex.org/W2034841618","https://openalex.org/W2037851654","https://openalex.org/W2040049280","https://openalex.org/W2046554800","https://openalex.org/W2092617083","https://openalex.org/W2095174088","https://openalex.org/W2097757574","https://openalex.org/W2098631313","https://openalex.org/W2105535594","https://openalex.org/W2107686700","https://openalex.org/W2122111042","https://openalex.org/W2122496402","https://openalex.org/W2142339769","https://openalex.org/W2143821562","https://openalex.org/W2147169507","https://openalex.org/W2151537585","https://openalex.org/W2158724449","https://openalex.org/W2166165501","https://openalex.org/W2177090493","https://openalex.org/W2182722412","https://openalex.org/W2536103954","https://openalex.org/W2604216405","https://openalex.org/W3036281070","https://openalex.org/W4231029117","https://openalex.org/W4235456164","https://openalex.org/W4243637616","https://openalex.org/W4244238212","https://openalex.org/W4252684946","https://openalex.org/W4255655703","https://openalex.org/W6601672434","https://openalex.org/W6641784307","https://openalex.org/W6680780905","https://openalex.org/W6686293579"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W4296359239","https://openalex.org/W1557905920","https://openalex.org/W2043093291","https://openalex.org/W1788667622"],"abstract_inverted_index":{"The":[0,104],"k-nearest":[1],"neighbors":[2],"(kNN)":[3],"algorithm":[4,23],"is":[5],"one":[6],"of":[7,48,120,132,140],"the":[8,17,22,26,46,49,55,59,71,77,89,97,114,118,126,133,142],"most":[9],"popular":[10],"and":[11,35,86,99,129],"simplest":[12],"lazy":[13],"learners.":[14],"However,":[15],"as":[16,88],"training":[18,50],"dataset":[19,101],"becomes":[20],"larger,":[21],"suffers":[24],"from":[25,96],"following":[27],"drawbacks:":[28],"large":[29],"storage":[30],"requirements,":[31],"slow":[32],"classification":[33,127],"speed,":[34],"high":[36],"sensitivity":[37],"to":[38,138],"noise.":[39],"To":[40],"overcome":[41],"these":[42],"drawbacks,":[43],"we":[44],"reduce":[45],"size":[47],"data":[51],"by":[52],"only":[53],"selecting":[54],"necessary":[56],"prototypes":[57],"before":[58],"classification.":[60],"This":[61],"study":[62],"proposes":[63],"an":[64],"extended":[65],"prototype":[66,83,144],"selection":[67,84,145],"technique":[68],"based":[69],"on":[70],"geometric":[72],"median":[73],"(GM).":[74],"We":[75,92],"compare":[76],"proposed":[78,105,134],"method":[79,106,135],"with":[80],"seven":[81],"state-of-the-art":[82,143],"methods":[85,146],"1NN":[87],"baseline":[90,115],"model.":[91],"use":[93],"25":[94],"datasets":[95],"KEEL":[98],"UCI":[100],"repository":[102],"website.":[103],"runs":[107],"at":[108,117],"least":[109],"3.5":[110],"times":[111],"faster":[112],"than":[113],"model":[116],"cost":[119],"slightly":[121],"reduced":[122],"accuracy.":[123],"In":[124],"addition,":[125],"accuracy":[128],"kappa":[130],"value":[131],"are":[136],"comparable":[137],"those":[139],"all":[141],"considered.":[147]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
