{"id":"https://openalex.org/W7165798645","doi":"https://doi.org/10.48550/arxiv.2606.24625","title":"QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification","display_name":"QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165798645","doi":"https://doi.org/10.48550/arxiv.2606.24625"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24625","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.24625","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139254340","display_name":"Parth Upman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Upman, Parth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139222755","display_name":"Shreyank N Gowda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gowda, Shreyank N","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9606999754905701,"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":0.9606999754905701,"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":0.009600000455975533,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.004399999976158142,"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/oversampling","display_name":"Oversampling","score":0.9215999841690063},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4878999888896942},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.4375},{"id":"https://openalex.org/keywords/neighbourhood","display_name":"Neighbourhood (mathematics)","score":0.4180999994277954},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40450000762939453},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.3919000029563904},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.38370001316070557},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.3580000102519989}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.9215999841690063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.595300018787384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5091999769210815},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5051000118255615},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4878999888896942},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.4375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42329999804496765},{"id":"https://openalex.org/C161677786","wikidata":"https://www.wikidata.org/wiki/Q2478475","display_name":"Neighbourhood (mathematics)","level":2,"score":0.4180999994277954},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.38370001316070557},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.35690000653266907},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C136643341","wikidata":"https://www.wikidata.org/wiki/Q1361526","display_name":"Reachability","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3160000145435333},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3068000078201294},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.28060001134872437},{"id":"https://openalex.org/C34130140","wikidata":"https://www.wikidata.org/wiki/Q1645406","display_name":"Midpoint","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.265500009059906}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24625","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.24625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24625","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6280921697616577}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Class":[0],"imbalance":[1],"poses":[2],"a":[3,29,39],"significant":[4],"challenge":[5],"in":[6,19,122],"classification,":[7],"where":[8],"existing":[9],"methods":[10],"such":[11],"as":[12],"SMOTE":[13],"often":[14],"generate":[15],"low-quality":[16],"synthetic":[17,99,168],"samples":[18,100],"regions":[20],"with":[21,72,103,151],"noise":[22],"or":[23],"class":[24],"overlap.":[25],"We":[26],"propose":[27],"QC-SMOTE,":[28],"quality-controlled":[30],"oversampling":[31,149],"framework":[32],"that":[33,63,136],"estimates":[34],"minority":[35,105],"sample":[36,76],"reliability":[37,77],"using":[38,58,131],"composite":[40],"neighbourhood":[41,108],"trustworthiness":[42],"score":[43],"combining":[44],"local":[45,95],"density,":[46],"safe-level,":[47],"and":[48,78,90,143,157],"isolation":[49],"from":[50],"the":[51,139,147,163],"majority":[52,70],"class.":[53],"Synthetic":[54],"candidates":[55],"are":[56,101],"generated":[57],"an":[59,112],"IPQ-guided":[60],"best-of-K":[61],"strategy":[62],"evaluates":[64],"midpoint":[65],"purity":[66,109],"and,":[67],"when":[68,107],"required,":[69],"clearance,":[71],"allocation":[73],"guided":[74],"by":[75,118],"boundary":[79],"informativeness.":[80],"Generation":[81],"behaviour":[82],"adapts":[83],"across":[84],"overlap--imbalance":[85],"regimes,":[86],"adjusting":[87],"interpolation":[88],"range":[89],"selection":[91],"criteria":[92],"to":[93,120],"match":[94],"data":[96],"geometry.":[97],"Low-quality":[98],"replaced":[102],"original":[104],"duplicates":[106],"falls":[110],"below":[111],"adaptive":[113],"threshold,":[114],"providing":[115],"graceful":[116],"degradation":[117],"reverting":[119],"duplication":[121],"severely":[123],"noisy":[124],"regions.":[125],"Experiments":[126],"on":[127],"30":[128],"imbalanced":[129,172],"datasets":[130],"repeated":[132],"stratified":[133],"cross-validation":[134],"show":[135],"QC-SMOTE":[137],"achieves":[138],"strongest":[140],"average":[141],"AUC-ROC":[142],"Macro":[144],"F1":[145],"among":[146],"compared":[148],"methods,":[150],"particularly":[152],"clear":[153],"gains":[154],"under":[155],"moderate":[156],"severe":[158],"imbalance.":[159],"These":[160],"results":[161],"demonstrate":[162],"importance":[164],"of":[165],"quality-aware,":[166],"geometry-adaptive":[167],"sampling":[169],"for":[170],"robust":[171],"classification.":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-25T00:00:00"}
