{"id":"https://openalex.org/W7129105916","doi":"https://doi.org/10.48550/arxiv.2602.13045","title":"Geometric Manifold Rectification for Imbalanced Learning","display_name":"Geometric Manifold Rectification for Imbalanced Learning","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7129105916","doi":"https://doi.org/10.48550/arxiv.2602.13045"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.13045","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065467620","display_name":"Xubin Wang","orcid":"https://orcid.org/0000-0001-6217-1305"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xubin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126089097","display_name":"Qing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5109574954","display_name":"Weijia Jia","orcid":"https://orcid.org/0000-0001-5628-6237"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Weijia","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":0.8942999839782715,"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":0.8942999839782715,"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":0.024800000712275505,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.012600000016391277,"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":0.6161999702453613},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.6078000068664551},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5023000240325928},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4986000061035156},{"id":"https://openalex.org/keywords/manifold-alignment","display_name":"Manifold alignment","score":0.45249998569488525},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.4262000024318695},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.42089998722076416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4122999906539917},{"id":"https://openalex.org/keywords/geometric-data-analysis","display_name":"Geometric data analysis","score":0.4081999957561493}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.6161999702453613},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.6078000068664551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5044999718666077},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4986000061035156},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4763000011444092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4625999927520752},{"id":"https://openalex.org/C153120616","wikidata":"https://www.wikidata.org/wiki/Q17068315","display_name":"Manifold alignment","level":4,"score":0.45249998569488525},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.4262000024318695},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4122999906539917},{"id":"https://openalex.org/C136520226","wikidata":"https://www.wikidata.org/wiki/Q302814","display_name":"Geometric data analysis","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4023999869823456},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C2778994249","wikidata":"https://www.wikidata.org/wiki/Q2842324","display_name":"TRAC","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3528999984264374},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C2776477805","wikidata":"https://www.wikidata.org/wiki/Q4460773","display_name":"Topological data analysis","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.29919999837875366},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28760001063346863},{"id":"https://openalex.org/C50942859","wikidata":"https://www.wikidata.org/wiki/Q4967193","display_name":"Rectification","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C71564387","wikidata":"https://www.wikidata.org/wiki/Q249514","display_name":"Chebyshev's inequality","level":5,"score":0.2542000114917755}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.13045","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.13045","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.13045","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":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.13045","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.6009755730628967,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Imbalanced":[0],"classification":[1],"presents":[2],"a":[3,22,87,141],"formidable":[4],"challenge":[5],"in":[6,29],"machine":[7],"learning,":[8],"particularly":[9],"when":[10],"tabular":[11],"datasets":[12,152],"are":[13],"plagued":[14],"by":[15,97],"noise":[16],"and":[17,61,71,125],"overlapping":[18],"class":[19,36],"boundaries.":[20],"From":[21],"geometric":[23,100],"perspective,":[24],"the":[25,30,34,38,43,67],"core":[26],"difficulty":[27],"lies":[28],"topological":[31],"intrusion":[32],"of":[33],"majority":[35,133],"into":[37],"minority":[39,76,138,145],"manifold,":[40],"which":[41],"obscures":[42],"true":[44],"decision":[45],"boundary.":[46],"Traditional":[47],"undersampling":[48],"techniques,":[49],"such":[50],"as":[51],"Edited":[52],"Nearest":[53],"Neighbours":[54],"(ENN),":[55],"typically":[56],"employ":[57],"symmetric":[58],"cleaning":[59,128],"rules":[60],"uniform":[62],"voting,":[63],"failing":[64],"to":[65,91,121],"capture":[66,122],"local":[68,99,123],"manifold":[69],"structure":[70],"often":[72],"inadvertently":[73],"removing":[74],"informative":[75],"samples.":[77],"In":[78],"this":[79],"paper,":[80],"we":[81],"propose":[82],"GMR":[83,102,155],"(Geometric":[84],"Manifold":[85],"Rectification),":[86],"novel":[88],"framework":[89],"designed":[90],"robustly":[92],"handle":[93],"imbalanced":[94],"structured":[95],"data":[96],"exploiting":[98],"priors.":[101],"makes":[103],"two":[104],"contributions:":[105],"(1)":[106],"Geometric":[107],"confidence":[108],"estimation":[109],"that":[110,129,154],"uses":[111],"inverse-distance":[112],"weighted":[113],"kNN":[114],"voting":[115],"with":[116,158],"an":[117],"adaptive":[118],"distance":[119],"metric":[120],"reliability;":[124],"(2)":[126],"asymmetric":[127],"is":[130,156],"strict":[131],"on":[132,144,149],"samples":[134,139],"while":[135],"conservatively":[136],"protecting":[137],"via":[140],"safe-guarding":[142],"cap":[143],"removal.":[146],"Extensive":[147],"experiments":[148],"multiple":[150],"benchmark":[151],"show":[153],"competitive":[157],"strong":[159],"sampling":[160],"baselines.":[161]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-02-17T00:00:00"}
