{"id":"https://openalex.org/W4416214787","doi":"https://doi.org/10.1109/access.2025.3632919","title":"UkM: A Novel Undersampling Method Using Modified k-Medoids Algorithm","display_name":"UkM: A Novel Undersampling Method Using Modified k-Medoids Algorithm","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416214787","doi":"https://doi.org/10.1109/access.2025.3632919"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3632919","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3632919","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3632919","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092508813","display_name":"Duygu Selin Turan","orcid":"https://orcid.org/0000-0001-9881-6013"},"institutions":[{"id":"https://openalex.org/I41641357","display_name":"Ege University","ror":"https://ror.org/02eaafc18","country_code":"TR","type":"education","lineage":["https://openalex.org/I41641357"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Duygu Selin Turan","raw_affiliation_strings":["Department of Mathematics, Faculty of Science, Ege University, &#x0130;zmir, T&#x00FC;rkiye","Department of Mathematics, Faculy of Science, Ege University, Turkey"],"raw_orcid":"https://orcid.org/0000-0001-9881-6013","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Science, Ege University, &#x0130;zmir, T&#x00FC;rkiye","institution_ids":["https://openalex.org/I41641357"]},{"raw_affiliation_string":"Department of Mathematics, Faculy of Science, Ege University, Turkey","institution_ids":["https://openalex.org/I41641357"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5092508813"],"corresponding_institution_ids":["https://openalex.org/I41641357"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17400957,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"195319","last_page":"195331"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9596999883651733,"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":0.9596999883651733,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.010599999688565731,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.004800000227987766,"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/undersampling","display_name":"Undersampling","score":0.9896000027656555},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.8586999773979187},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5637000203132629},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5414000153541565},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.45739999413490295},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.44510000944137573},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3991999924182892}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.9896000027656555},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.8586999773979187},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7386999726295471},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5637000203132629},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5414000153541565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5131999850273132},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4528999924659729},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4212000072002411},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3991999924182892},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38190001249313354},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.37059998512268066},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3632919","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3632919","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:180f820c7cfb4fbe9ebfcbb42576ffba","is_oa":true,"landing_page_url":"https://doaj.org/article/180f820c7cfb4fbe9ebfcbb42576ffba","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 195319-195331 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3632919","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3632919","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"from":[1],"imbalanced":[2,53,100],"data":[3,85],"remains":[4],"a":[5,28,35,56,149,182,215],"persistent":[6],"challenge":[7],"in":[8,13,228,254,274,326],"classification":[9,232],"tasks,":[10],"often":[11],"resulting":[12],"biased":[14],"model":[15],"performance":[16,168,201,205,227,290,338],"and":[17,59,74,102,114,130,135,146,156,211,225,234,237,243,249,258,265,285],"poor":[18],"generalization,":[19],"particularly":[20,218],"for":[21],"the":[22,62,71,77,127,153,157,165,188,191,198,297,300,306,320,341],"minority":[23,255],"class.":[24],"This":[25],"study":[26],"proposes":[27],"novel":[29],"undersampling":[30,67,107,220,314],"technique,":[31],"UkM,":[32],"which":[33],"integrates":[34],"modified":[36],"k-medoids":[37],"clustering":[38],"algorithm":[39],"to":[40,50,83,163,263],"effectively":[41],"balance":[42,51,261],"class":[43,79,89,238,256,260],"distributions.":[44],"The":[45,91,122,178,268],"goal":[46],"of":[47,167,190,230,252,299],"UkM":[48,69,222,284,310],"is":[49,206,278],"an":[52,118],"dataset":[54,73,176],"using":[55,126,132],"clustering-based":[57],"approach":[58],"then":[60],"classify":[61],"balanced":[63,125,231],"dataset.":[64],"Unlike":[65],"conventional":[66],"methods,":[68,329],"clusters":[70],"majority":[72,78],"selectively":[75],"reduces":[76],"within":[80],"each":[81],"cluster":[82],"preserve":[84],"structure":[86],"while":[87,246,303],"improving":[88],"balance.":[90],"proposed":[92],"method":[93],"was":[94,161],"evaluated":[95],"on":[96,173,187,202,241,296],"32":[97],"publicly":[98],"available":[99],"datasets":[101,123],"compared":[103,128],"with":[104,139],"seven":[105],"established":[106],"techniques\u2014DBU,":[108],"PUMD,":[109],"NearMiss,":[110],"RUS,":[111],"UBMD,":[112],"CLbU,":[113],"FCA\u2014as":[115],"well":[116],"as":[117,214],"oversampling":[119],"method,":[120],"SMOTE.":[121],"were":[124,171],"methods":[129,315],"analyzed":[131],"Random":[133],"Forest":[134],"Multilayer":[136],"Perceptron":[137],"classifier":[138],"four":[140],"evaluation":[141],"metrics:":[142],"F1-Score,":[143],"Recall,":[144],"G-mean,":[145],"AUC.":[147],"Furthermore,":[148],"statistical":[150],"analysis":[151],"involving":[152],"Friedman":[154,321],"test":[155,160,322],"Nemenyi":[158,269,331],"post-hoc":[159,270],"conducted":[162],"determine":[164],"significance":[166],"differences.":[169],"Computations":[170],"performed":[172],"two":[174],"different":[175],"groups.":[177],"second":[179,301],"group":[180],"has":[181],"higher":[183],"imbalance":[184],"ratio.":[185],"Based":[186,295],"results":[189,298],"first":[192],"group,":[193,302],"although":[194],"SMOTE":[195,304],"generally":[196],"demonstrates":[197,223],"highest":[199,307],"overall":[200,235,308],"average,":[203],"UkM\u2019s":[204],"noteworthy":[207],"across":[208,316],"multiple":[209],"metrics":[210],"stands":[212],"out":[213],"prominent":[216],"alternative,":[217],"among":[219,328],"methods.":[221],"high":[224],"consistent":[226],"terms":[229],"success":[233],"accuracy":[236],"discrimination":[239],"based":[240],"F-score":[242,327],"AUC":[244],"results,":[245],"exhibiting":[247],"inconsistency":[248],"potential":[250],"risk":[251],"failure":[253],"recognition":[257],"maintaining":[259],"according":[262],"Recall":[264],"G-Mean":[266],"metrics.":[267,318],"analyses":[271],"reveal":[272],"that":[273,288],"most":[275],"cases,":[276],"there":[277],"no":[279],"statistically":[280],"significant":[281,324],"difference":[282],"between":[283,340],"SMOTE,":[286],"indicating":[287],"their":[289],"can":[291],"be":[292],"considered":[293],"comparable.":[294],"shows":[305],"performance,":[309],"frequently":[311],"outperforms":[312],"other":[313],"several":[317],"Although":[319],"indicates":[323],"differences":[325],"pairwise":[330],"tests":[332],"mostly":[333],"do":[334],"not,":[335],"suggesting":[336],"comparable":[337],"levels":[339],"models.":[342]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-11-14T00:00:00"}
