{"id":"https://openalex.org/W2896336565","doi":"https://doi.org/10.1109/ijcnn.2018.8489411","title":"Improving kNN classification under Unbalanced Data. A New Geometric Oversampling Approach","display_name":"Improving kNN classification under Unbalanced Data. A New Geometric Oversampling Approach","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2896336565","doi":"https://doi.org/10.1109/ijcnn.2018.8489411","mag":"2896336565"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489411","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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/A5083114464","display_name":"A. M. Carvalho","orcid":"https://orcid.org/0000-0002-8801-4321"},"institutions":[{"id":"https://openalex.org/I71715416","display_name":"Universidade Federal do ABC","ror":"https://ror.org/028kg9j04","country_code":"BR","type":"education","lineage":["https://openalex.org/I71715416"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Alexandre Miguel de Carvalho","raw_affiliation_strings":["Center of Math, Computing and Cognition (CMCC), Universidade Federal do ABC (UFABC), Santo Andr\u00e9, Brazil"],"affiliations":[{"raw_affiliation_string":"Center of Math, Computing and Cognition (CMCC), Universidade Federal do ABC (UFABC), Santo Andr\u00e9, Brazil","institution_ids":["https://openalex.org/I71715416"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005717519","display_name":"Ronaldo C. Prati","orcid":"https://orcid.org/0000-0001-8597-4987"},"institutions":[{"id":"https://openalex.org/I71715416","display_name":"Universidade Federal do ABC","ror":"https://ror.org/028kg9j04","country_code":"BR","type":"education","lineage":["https://openalex.org/I71715416"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ronaldo Cristiano Prati","raw_affiliation_strings":["Center of Math, Computing and Cognition (CMCC), Universidade Federal do ABC (UFABC), Santo Andr\u00e9, Brazil"],"affiliations":[{"raw_affiliation_string":"Center of Math, Computing and Cognition (CMCC), Universidade Federal do ABC (UFABC), Santo Andr\u00e9, Brazil","institution_ids":["https://openalex.org/I71715416"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083114464"],"corresponding_institution_ids":["https://openalex.org/I71715416"],"apc_list":null,"apc_paid":null,"fwci":0.6769,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.78099631,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"18","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","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/T11652","display_name":"Imbalanced Data Classification Techniques","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/T12535","display_name":"Machine Learning and Data Classification","score":0.9947999715805054,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7135138511657715},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.6856498122215271},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5731518864631653},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5576949715614319},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5085195302963257},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46334654092788696},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4542830288410187},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4300386607646942},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13838818669319153},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.10093158483505249}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7135138511657715},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.6856498122215271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5731518864631653},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5576949715614319},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5085195302963257},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46334654092788696},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4542830288410187},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4300386607646942},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13838818669319153},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.10093158483505249},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489411","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","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":25,"referenced_works":["https://openalex.org/W1496056137","https://openalex.org/W1580791986","https://openalex.org/W1969732712","https://openalex.org/W1986959058","https://openalex.org/W2020984122","https://openalex.org/W2073645537","https://openalex.org/W2092452774","https://openalex.org/W2101234009","https://openalex.org/W2120194796","https://openalex.org/W2124246957","https://openalex.org/W2133893370","https://openalex.org/W2148143831","https://openalex.org/W2149906774","https://openalex.org/W2153504150","https://openalex.org/W2165370158","https://openalex.org/W2477734150","https://openalex.org/W2512691369","https://openalex.org/W2517034999","https://openalex.org/W2521200999","https://openalex.org/W2562319768","https://openalex.org/W2582743722","https://openalex.org/W4210521336","https://openalex.org/W4230887410","https://openalex.org/W6675354045","https://openalex.org/W6725966628"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W2781247653","https://openalex.org/W3196098778","https://openalex.org/W3211250490","https://openalex.org/W4360584310","https://openalex.org/W2953675148","https://openalex.org/W2981515171","https://openalex.org/W80466363","https://openalex.org/W2924282518","https://openalex.org/W3080872054"],"abstract_inverted_index":{"Training":[0],"classifiers":[1],"with":[2,111],"unbalanced":[3],"data":[4],"is":[5,34,61],"one":[6,30],"of":[7,14,31,44,56,102,104],"the":[8,12,54,97,105],"main":[9],"challenges":[10],"in":[11,96],"field":[13],"Machine":[15],"Learning.":[16],"Some":[17],"techniques":[18],"that":[19,67],"try":[20],"to":[21,53,73,81],"get":[22],"around":[23],"this":[24,88],"problem":[25],"have":[26],"been":[27],"proposed,":[28],"where":[29],"most":[32],"important":[33],"SMOTE,":[35],"which":[36],"artificially":[37],"generates":[38],"new":[39,51,75,89],"instances":[40],"by":[41,70],"interpolating":[42],"pairs":[43],"original":[45],"instances.":[46,77],"This":[47,78],"paper":[48],"proposes":[49],"a":[50,62],"approach":[52],"balancing":[55],"classifier":[57],"training":[58],"data.":[59],"It":[60],"geometric":[63],"and":[64],"spatial":[65],"approach,":[66],"uses":[68],"tetrahedralization":[69],"Delaunay":[71],"tessellation":[72],"generate":[74],"artificial":[76],"allow":[79],"us":[80],"go":[82],"beyond":[83],"single":[84],"pair":[85],"interpolation.":[86],"Applying":[87],"method,":[90],"we":[91],"can":[92],"notice":[93],"an":[94],"improvement":[95],"classification":[98,107],"quality":[99],"(in":[100],"terms":[101],"AUC)":[103],"kNN":[106],"algorithm":[108],"when":[109],"compared":[110],"SMOTE.":[112]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}