{"id":"https://openalex.org/W7156636665","doi":"https://doi.org/10.48550/arxiv.2604.24096","title":"Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification","display_name":"Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification","publication_year":2026,"publication_date":"2026-04-27","ids":{"openalex":"https://openalex.org/W7156636665","doi":"https://doi.org/10.48550/arxiv.2604.24096"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.24096","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24096","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.24096","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085336408","display_name":"June-Woo Kim","orcid":"https://orcid.org/0000-0003-0111-300X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, June-Woo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042504231","display_name":"Miika Toikkanen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Toikkanen, Miika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111025822","display_name":"Heejoon Koo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koo, Heejoon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134096581","display_name":"Yoon Tae Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Yoon Tae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134807402","display_name":"Doyoung Kwon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kwon, Doyoung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101709142","display_name":"Kyunghoon Kim","orcid":"https://orcid.org/0000-0002-0707-6839"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Kyunghoon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9477999806404114,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9477999806404114,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10863","display_name":"Voice and Speech Disorders","score":0.007799999788403511,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11260","display_name":"Tracheal and airway disorders","score":0.00559999980032444,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8680999875068665},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.6121000051498413},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5806000232696533},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5525000095367432},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.47350001335144043},{"id":"https://openalex.org/keywords/multiple-models","display_name":"Multiple Models","score":0.41769999265670776},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.39660000801086426}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8680999875068665},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6240000128746033},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.6121000051498413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.586899995803833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5852000117301941},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5525000095367432},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.47350001335144043},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.38260000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3637000024318695},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.24096","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24096","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.24096","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.24096","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"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":{"Training":[0],"reliable":[1],"respiratory":[2],"sound":[3],"classification":[4],"models":[5,26,32,63,80],"remains":[6],"challenging":[7],"due":[8],"to":[9,34,115,145],"the":[10,43,82,113,125],"limited":[11],"size":[12],"and":[13,36,68,93,103,133],"subject":[14],"diversity":[15,59,107],"of":[16,45,131],"datasets.":[17],"Ensemble":[18],"methods":[19],"can":[20],"improve":[21],"robustness,":[22],"but":[23],"when":[24],"base":[25,62,79,109],"are":[27],"trained":[28,74],"on":[29,64,81,124,137],"identical":[30],"data,":[31],"tend":[33],"overfit":[35],"produce":[37],"highly":[38],"correlated":[39],"predictions,":[40],"thereby":[41],"reducing":[42],"effectiveness":[44],"ensembling.":[46],"In":[47],"this":[48],"work,":[49],"we":[50,77],"investigate":[51],"a":[52,73,129],"meta-ensemble":[53],"learning":[54],"methodology":[55],"that":[56],"enhances":[57],"prediction":[58],"by":[60],"training":[61],"diverse":[65],"data":[66,87,99],"splits":[67],"combining":[69],"their":[70],"outputs":[71],"through":[72],"meta-model.":[75],"Specifically,":[76],"train":[78],"ICBHI":[83,126],"dataset":[84],"using":[85],"two":[86,98,138],"split":[88,92],"settings:":[89,101],"fixed":[90],"80-20%":[91],"five-fold":[94],"cross-validation":[95],"split,":[96],"under":[97],"granularity":[100],"patient-":[102],"sample-level.":[104],"The":[105],"resulting":[106],"in":[108],"model":[110],"predictions":[111],"enables":[112],"meta-model":[114],"better":[116],"generalize.":[117],"Our":[118],"approach":[119],"achieves":[120],"new":[121],"state-of-the-art":[122],"performance":[123],"benchmark,":[127],"reaching":[128],"Score":[130],"66.49%":[132],"showing":[134],"improved":[135],"generalization":[136],"out-of-distribution":[139],"datasets,":[140],"indicating":[141],"its":[142],"potential":[143],"applicability":[144],"real-world":[146],"clinical":[147],"data.":[148]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-29T00:00:00"}