{"id":"https://openalex.org/W3045247618","doi":"https://doi.org/10.3233/his-200280","title":"Using the concept of instance typicality in instance-based learning environments involving nominal attributes","display_name":"Using the concept of instance typicality in instance-based learning environments involving nominal attributes","publication_year":2020,"publication_date":"2020-07-27","ids":{"openalex":"https://openalex.org/W3045247618","doi":"https://doi.org/10.3233/his-200280","mag":"3045247618"},"language":"en","primary_location":{"id":"doi:10.3233/his-200280","is_oa":false,"landing_page_url":"https://doi.org/10.3233/his-200280","pdf_url":null,"source":{"id":"https://openalex.org/S50927259","display_name":"International Journal of Hybrid Intelligent Systems","issn_l":"1448-5869","issn":["1448-5869","1875-8819"],"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":"International Journal of Hybrid Intelligent Systems","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/A5083806833","display_name":"S. V. Gon\u00e7alves","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S.V. Gon\u00e7alves","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5113765429","display_name":"M. C. Nicoletti","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"M.C. Nicoletti","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5113765429"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2709,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64071709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"16","issue":"2","first_page":"67","last_page":"79"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9866999983787537,"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":0.9850000143051147,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7468394041061401},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6239480972290039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.614473283290863},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.585128903388977},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5329820513725281},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5328949689865112},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5052052140235901},{"id":"https://openalex.org/keywords/instance-based-learning","display_name":"Instance-based learning","score":0.439853310585022},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.43400466442108154},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34697723388671875},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.23861655592918396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468394041061401},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6239480972290039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.614473283290863},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.585128903388977},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5329820513725281},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5328949689865112},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5052052140235901},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.439853310585022},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.43400466442108154},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34697723388671875},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.23861655592918396},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/his-200280","is_oa":false,"landing_page_url":"https://doi.org/10.3233/his-200280","pdf_url":null,"source":{"id":"https://openalex.org/S50927259","display_name":"International Journal of Hybrid Intelligent Systems","issn_l":"1448-5869","issn":["1448-5869","1875-8819"],"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":"International Journal of Hybrid Intelligent Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W23284400","https://openalex.org/W1483055888","https://openalex.org/W1532325895","https://openalex.org/W1535540078","https://openalex.org/W1563946824","https://openalex.org/W1594924988","https://openalex.org/W1990643970","https://openalex.org/W1994410331","https://openalex.org/W2000255081","https://openalex.org/W2003048487","https://openalex.org/W2004131797","https://openalex.org/W2022518678","https://openalex.org/W2042587746","https://openalex.org/W2067098334","https://openalex.org/W2085770564","https://openalex.org/W2096729078","https://openalex.org/W2098631313","https://openalex.org/W2107686700","https://openalex.org/W2122111042","https://openalex.org/W2122496402","https://openalex.org/W2135251234","https://openalex.org/W2142827986","https://openalex.org/W2147169507","https://openalex.org/W2158724449","https://openalex.org/W2162583212","https://openalex.org/W2163952039","https://openalex.org/W2182722412","https://openalex.org/W2398688360","https://openalex.org/W2467200550","https://openalex.org/W2510620627","https://openalex.org/W2520858206","https://openalex.org/W2799061466","https://openalex.org/W3049749774","https://openalex.org/W3085162807","https://openalex.org/W4213009331","https://openalex.org/W4242221364","https://openalex.org/W4244238212","https://openalex.org/W4247640718","https://openalex.org/W6600964755","https://openalex.org/W6679951313","https://openalex.org/W6712514217"],"related_works":["https://openalex.org/W3196155444","https://openalex.org/W2962840010","https://openalex.org/W4286799911","https://openalex.org/W3033485676","https://openalex.org/W4319309271","https://openalex.org/W4306321456","https://openalex.org/W4224009465","https://openalex.org/W4321844043","https://openalex.org/W4286629047","https://openalex.org/W1855693757"],"abstract_inverted_index":{"Instance-Based":[0],"Learning":[1],"(IBL)":[2],"is":[3,102,116,151,180],"a":[4,60,66,168,205,216],"machine":[5],"learning":[6],"research":[7],"area":[8],"with":[9,133],"focus":[10],"on":[11,82,153,208],"supervised":[12],"algorithms":[13],"that":[14,73,204,231],"use":[15],"the":[16,21,24,28,32,56,83,99,137,141,154,174,190,194,200,209,229],"given":[17],"training":[18,29],"set":[19,33],"as":[20,104,167],"expression":[22],"of":[23,38,69,78,91,98,108,140,156,158,211],"learned":[25],"concept.":[26],"Usually":[27],"instances":[30,75,224],"in":[31,171],"are":[34],"described":[35],"by":[36,50,199],"vectors":[37],"attribute":[39,107],"values":[40,84],"and":[41,114,126,193],"an":[42,51,128,159],"associated":[43],"class.":[44,71],"The":[45,148],"general":[46],"ization":[47],"process":[48],"conducted":[49],"instance-based":[52,143],"algorithm":[53,144],"happens":[54],"during":[55,136],"classification":[57,138],"phase,":[58],"when":[59,222],"class":[61],"should":[62],"be":[63,77,90,163,178,215],"assigned":[64],"to":[65,177,219],"new":[67,175],"instance":[68,176,212],"unknown":[70],"Attributes":[72],"describe":[74,232],"can":[76,89,162,214],"different":[79],"types,":[80],"depending":[81],"they":[85],"represent":[86],"and,":[87],"usually,":[88],"discrete":[92,100],"or":[93],"continuous":[94],"type.":[95],"A":[96],"subtype":[97],"type":[101,110],"known":[103],"nominal.":[105],"An":[106],"nominal":[109,134,226],"usually":[111],"represents":[112],"categories":[113],"there":[115],"no":[117],"order":[118],"among":[119,228],"its":[120],"possible":[121,169],"values.":[122],"This":[123],"paper":[124],"proposes":[125],"investigates":[127],"alternative":[129],"strategy":[130,150,192,197,206],"for":[131],"dealing":[132],"attributes":[135,227,230],"phase":[139],"well-known":[142],"NN":[145,202],"(Nearest":[146],"Neighbor).":[147],"proposed":[149,191],"based":[152,207],"concept":[155,210],"typicality":[157,213],"instance,":[160],"which":[161],"taken":[164],"into":[165],"account":[166],"tiebreaker,":[170],"situations":[172],"where":[173],"classified":[179],"equidistant":[181],"from":[182],"more":[183],"than":[184],"one":[185],"nearest":[186],"neighbor.":[187],"Experiments":[188],"using":[189],"default":[195],"random":[196],"used":[198],"conventional":[201],"show":[203],"convenient":[217],"choice":[218],"improve":[220],"accuracy,":[221],"data":[223],"have":[225],"them.":[233]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-23T06:36:01.041984","created_date":"2025-10-10T00:00:00"}
