{"id":"https://openalex.org/W2897454155","doi":"https://doi.org/10.1109/ijcnn.2018.8489320","title":"Self\u2013Organizing\u2013Maps Based Undersampling for the Classification of Unbalanced Datasets","display_name":"Self\u2013Organizing\u2013Maps Based Undersampling for the Classification of Unbalanced Datasets","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897454155","doi":"https://doi.org/10.1109/ijcnn.2018.8489320","mag":"2897454155"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489320","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":"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/A5070598302","display_name":"Marco Vannucci","orcid":"https://orcid.org/0000-0002-6115-1588"},"institutions":[{"id":"https://openalex.org/I162290304","display_name":"Scuola Superiore Sant'Anna","ror":"https://ror.org/025602r80","country_code":"IT","type":"education","lineage":["https://openalex.org/I162290304"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marco Vannucci","raw_affiliation_strings":["Scuola Superiore Sant\u2019Anna, TeCIP institute, Via G. Moruzzi 1, Pisa, Italy","Scuola Superiore Sant'Anna, TeCIP institute, Via G. Moruzzi 1, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Scuola Superiore Sant\u2019Anna, TeCIP institute, Via G. Moruzzi 1, Pisa, Italy","institution_ids":["https://openalex.org/I162290304"]},{"raw_affiliation_string":"Scuola Superiore Sant'Anna, TeCIP institute, Via G. Moruzzi 1, Pisa, Italy","institution_ids":["https://openalex.org/I162290304"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033400760","display_name":"Valentina Colla","orcid":"https://orcid.org/0000-0002-9574-0575"},"institutions":[{"id":"https://openalex.org/I162290304","display_name":"Scuola Superiore Sant'Anna","ror":"https://ror.org/025602r80","country_code":"IT","type":"education","lineage":["https://openalex.org/I162290304"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Valentina Colla","raw_affiliation_strings":["Scuola Superiore Sant\u2019Anna, TeCIP institute, Via G. Moruzzi 1, Pisa, Italy","Scuola Superiore Sant'Anna, TeCIP institute, Via G. Moruzzi 1, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Scuola Superiore Sant\u2019Anna, TeCIP institute, Via G. Moruzzi 1, Pisa, Italy","institution_ids":["https://openalex.org/I162290304"]},{"raw_affiliation_string":"Scuola Superiore Sant'Anna, TeCIP institute, Via G. Moruzzi 1, Pisa, Italy","institution_ids":["https://openalex.org/I162290304"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162290304"],"apc_list":null,"apc_paid":null,"fwci":0.7923,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.78213748,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"12","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":0.9961000084877014,"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":0.9961000084877014,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9799000024795532,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.978600025177002,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.8725137114524841},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7788426876068115},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6944230794906616},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.6430607438087463},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5989084243774414},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5843687057495117},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5836763978004456},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5787906646728516},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5283352136611938},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4549597501754761},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4480500817298889},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.44799700379371643},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.18608811497688293}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.8725137114524841},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7788426876068115},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6944230794906616},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6430607438087463},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5989084243774414},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5843687057495117},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5836763978004456},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5787906646728516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5283352136611938},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4549597501754761},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4480500817298889},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.44799700379371643},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.18608811497688293},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489320","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"},{"id":"pmh:oai:www.iris.sssup.it:11382/522906","is_oa":false,"landing_page_url":"http://hdl.handle.net/11382/522906","pdf_url":null,"source":{"id":"https://openalex.org/S4377196376","display_name":"CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I162290304","host_organization_name":"Scuola Superiore Sant'Anna","host_organization_lineage":["https://openalex.org/I162290304"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W85350352","https://openalex.org/W1541950099","https://openalex.org/W1580049798","https://openalex.org/W1885262319","https://openalex.org/W1965895350","https://openalex.org/W1966748751","https://openalex.org/W1976597329","https://openalex.org/W1993220166","https://openalex.org/W2023639956","https://openalex.org/W2038030840","https://openalex.org/W2099454382","https://openalex.org/W2118978333","https://openalex.org/W2133212356","https://openalex.org/W2137029138","https://openalex.org/W2148143831","https://openalex.org/W2151630539","https://openalex.org/W2161920802","https://openalex.org/W2469114737","https://openalex.org/W2548133570","https://openalex.org/W2594903377","https://openalex.org/W2618516756","https://openalex.org/W2734466259","https://openalex.org/W2794768378","https://openalex.org/W3120740533","https://openalex.org/W4285719527","https://openalex.org/W6603460400","https://openalex.org/W6632440209","https://openalex.org/W6634569365","https://openalex.org/W6682658635","https://openalex.org/W6683669014","https://openalex.org/W6729745395","https://openalex.org/W6749530183"],"related_works":["https://openalex.org/W2109073422","https://openalex.org/W4286422956","https://openalex.org/W2968770746","https://openalex.org/W4385322723","https://openalex.org/W2023639956","https://openalex.org/W3188632291","https://openalex.org/W2474028989","https://openalex.org/W3104213954","https://openalex.org/W4297857039","https://openalex.org/W2524699983"],"abstract_inverted_index":{"The":[0,30,85,105,122],"paper":[1],"describes":[2],"an":[3,18],"approach":[4,123],"for":[5,14],"the":[6,9,15,22,36,39,48,55,61,65,71,78,80,95,101,114,143],"preprocessing":[7],"of":[8,17,24,27,38,41,64],"dataset":[10,50],"to":[11,53,59,77,112,130],"be":[12],"used":[13],"training":[16,49,103],"arbitrary":[19],"classifier":[20,81],"in":[21,47,51,75,100,110],"context":[23],"binary":[25],"classification":[26],"unbalanced":[28],"datasets.":[29],"proposed":[31,86],"method":[32],"is":[33,82,119],"based":[34],"on":[35],"reduction":[37],"number":[40],"frequent":[42,98,115],"samples":[43,99,116],"that":[44,93],"are":[45,69,108],"included":[46],"order":[52,111],"mitigate":[54],"unbalance":[56],"rate":[57],"and":[58,97,128,146],"promote":[60],"correct":[62],"detection":[63],"rare":[66,96],"samples,":[67],"which":[68],"often":[70],"most":[72,120],"interesting":[73],"ones":[74],"relation":[76],"application":[79],"designed":[83],"for.":[84],"technique":[87],"exploits":[88],"two":[89],"Self":[90],"Organizing":[91],"Maps":[92],"clusterize":[94],"original":[102],"dataset.":[104],"obtained":[106],"clusterizations":[107],"exploited":[109],"determine":[113],"whose":[117],"removal":[118],"convenient.":[121],"has":[124],"been":[125],"successfully":[126],"tested":[127],"compared":[129],"other":[131],"well":[132],"known":[133],"resampling":[134],"techniques":[135],"by":[136],"exploiting":[137],"different":[138],"datasets":[139],"coming":[140],"from":[141],"both":[142],"UCI":[144],"repository":[145],"real":[147],"industrial":[148],"applications.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}