{"id":"https://openalex.org/W4415179630","doi":"https://doi.org/10.1109/blackseacom65655.2025.11193947","title":"KLSampler: A Kullback-Leibler Divergence Based Undersampling Technique for Machine Learning Applications","display_name":"KLSampler: A Kullback-Leibler Divergence Based Undersampling Technique for Machine Learning Applications","publication_year":2025,"publication_date":"2025-06-23","ids":{"openalex":"https://openalex.org/W4415179630","doi":"https://doi.org/10.1109/blackseacom65655.2025.11193947"},"language":"en","primary_location":{"id":"doi:10.1109/blackseacom65655.2025.11193947","is_oa":false,"landing_page_url":"https://doi.org/10.1109/blackseacom65655.2025.11193947","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","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/A5072399330","display_name":"Kaz\u0131m K\u0131van\u00e7 Eren","orcid":"https://orcid.org/0000-0002-5071-2722"},"institutions":[{"id":"https://openalex.org/I51826884","display_name":"Kocaeli \u00dcniversitesi","ror":"https://ror.org/0411seq30","country_code":"TR","type":"education","lineage":["https://openalex.org/I51826884"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Kaz\u0131m K\u0131van\u00e7 Eren","raw_affiliation_strings":["Kocaeli University,Department of Computer Engineering,Kocaeli,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kocaeli University,Department of Computer Engineering,Kocaeli,Turkey","institution_ids":["https://openalex.org/I51826884"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005657931","display_name":"Kerem K\u00fc\u00e7\u00fck","orcid":"https://orcid.org/0000-0002-2621-634X"},"institutions":[{"id":"https://openalex.org/I51826884","display_name":"Kocaeli \u00dcniversitesi","ror":"https://ror.org/0411seq30","country_code":"TR","type":"education","lineage":["https://openalex.org/I51826884"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Kerem K\u00fc\u00e7\u00fck","raw_affiliation_strings":["Kocaeli University,Department of Software Engineering,Kocaeli,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kocaeli University,Department of Software Engineering,Kocaeli,Turkey","institution_ids":["https://openalex.org/I51826884"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sema Zeynep Bulut","orcid":null},"institutions":[{"id":"https://openalex.org/I2799360972","display_name":"Hitit \u00dcniversitesi","ror":"https://ror.org/01x8m3269","country_code":"TR","type":"education","lineage":["https://openalex.org/I2799360972"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sema Zeynep Bulut","raw_affiliation_strings":["Hitit Computer Solutions,Department of Product Strategy,Istanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hitit Computer Solutions,Department of Product Strategy,Istanbul,Turkey","institution_ids":["https://openalex.org/I2799360972"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119998218","display_name":"Yi\u011fiter Yi\u011fit","orcid":null},"institutions":[{"id":"https://openalex.org/I2799360972","display_name":"Hitit \u00dcniversitesi","ror":"https://ror.org/01x8m3269","country_code":"TR","type":"education","lineage":["https://openalex.org/I2799360972"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Yi\u011fiter Yi\u011fit","raw_affiliation_strings":["Hitit Computer Solutions,Department of Product Strategy,Istanbul,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hitit Computer Solutions,Department of Product Strategy,Istanbul,Turkey","institution_ids":["https://openalex.org/I2799360972"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113701933","display_name":"Orhan Akbulut","orcid":null},"institutions":[{"id":"https://openalex.org/I51826884","display_name":"Kocaeli \u00dcniversitesi","ror":"https://ror.org/0411seq30","country_code":"TR","type":"education","lineage":["https://openalex.org/I51826884"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Orhan Akbulut","raw_affiliation_strings":["Kocaeli University,Department of Software Engineering,Kocaeli,Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kocaeli University,Department of Software Engineering,Kocaeli,Turkey","institution_ids":["https://openalex.org/I51826884"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.5030999779701233,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.5030999779701233,"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/T10320","display_name":"Neural Networks and Applications","score":0.4916999936103821,"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":0.4796000123023987,"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/undersampling","display_name":"Undersampling","score":0.9876000285148621},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.5569000244140625},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5109999775886536},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.487199991941452},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4855000078678131},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.46389999985694885},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42579999566078186},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41819998621940613},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.41370001435279846}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.9876000285148621},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6725999712944031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6169000267982483},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6161999702453613},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5569000244140625},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.487199991941452},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.46389999985694885},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.41370001435279846},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.375},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.3244999945163727},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3034000098705292},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.299699991941452},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2554999887943268}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/blackseacom65655.2025.11193947","is_oa":false,"landing_page_url":"https://doi.org/10.1109/blackseacom65655.2025.11193947","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","raw_type":"proceedings-article"},{"id":"pmh:fded4469-f7c9-4ec9-a9dd-51478dae466b","is_oa":false,"landing_page_url":"https://avesis.kocaeli.edu.tr/publication/details/fded4469-f7c9-4ec9-a9dd-51478dae466b/oai","pdf_url":null,"source":{"id":"https://openalex.org/S7407055291","display_name":"Kocaeli \u00dcniversitesi - AVES\u0130S","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1990970321","https://openalex.org/W1993220166","https://openalex.org/W2129905273","https://openalex.org/W2151537585","https://openalex.org/W2801922753","https://openalex.org/W3046141163","https://openalex.org/W3162956350","https://openalex.org/W4406610597","https://openalex.org/W4407950309","https://openalex.org/W4408100195"],"related_works":[],"abstract_inverted_index":{"Class":[0],"imbalance":[1],"poses":[2],"a":[3,148],"significant":[4],"challenge":[5],"in":[6,18,167],"binary":[7],"classification":[8],"tasks,":[9],"often":[10],"degrading":[11],"model":[12,165],"performance":[13],"and":[14,45,83,97,112,126,137,150,161],"limiting":[15],"generalization,":[16],"particularly":[17],"domains":[19],"where":[20],"minority":[21],"classes":[22],"are":[23,132],"critical.":[24],"Traditional":[25],"undersampling":[26,55,155],"techniques":[27],"tend":[28],"to":[29,42,63,153],"remove":[30],"majority":[31,74,99],"class":[32,100,120],"samples":[33],"without":[34],"considering":[35],"their":[36],"distributional":[37,70],"characteristics,":[38],"which":[39],"may":[40],"lead":[41],"information":[43],"loss":[44],"skewed":[46],"representations.":[47],"In":[48],"this":[49,65],"study,":[50],"we":[51],"propose":[52],"KLSampler,":[53],"an":[54],"algorithm":[56,77],"based":[57],"on":[58,109],"Kullback\u2013Leibler":[59],"(KL)":[60],"divergence,":[61],"designed":[62],"address":[64],"limitation":[66],"by":[67,89,103],"preserving":[68],"the":[69,73,86,91,95,142],"integrity":[71],"of":[72],"class.":[75],"The":[76],"begins":[78],"with":[79],"cluster-aware":[80],"proportional":[81],"sampling":[82],"iteratively":[84],"refines":[85],"selected":[87],"subset":[88],"minimizing":[90],"KL":[92],"divergence":[93],"between":[94],"original":[96],"sampled":[98],"distributions,":[101],"guided":[102],"kernel":[104],"density":[105],"estimation.":[106],"Experimental":[107],"evaluations":[108],"both":[110,135],"real-world":[111],"synthetic":[113],"datasets":[114],"demonstrate":[115],"that":[116,145],"KLSampler":[117,146],"achieves":[118],"improved":[119,158],"balance":[121],"while":[122],"maintaining":[123],"low":[124],"KL-divergence":[125],"minimal":[127],"covariate":[128],"shift.":[129],"These":[130],"outcomes":[131],"validated":[133],"using":[134],"divergence-based":[136],"domain":[138],"classifier":[139],"metrics.":[140],"Overall,":[141],"results":[143],"suggest":[144],"offers":[147],"robust":[149],"distribution-aware":[151],"alternative":[152],"conventional":[154],"techniques,":[156],"providing":[157],"data":[159],"quality":[160],"supporting":[162],"more":[163],"reliable":[164],"training":[166],"imbalanced":[168],"learning":[169],"scenarios.":[170]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-15T00:00:00"}