{"id":"https://openalex.org/W7220949666","doi":"https://doi.org/10.48550/arxiv.2610.09984","title":"Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative","display_name":"Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative","publication_year":2026,"publication_date":"2026-10-07","ids":{"openalex":"https://openalex.org/W7220949666","doi":"https://doi.org/10.48550/arxiv.2610.09984"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.09984","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.09984","pdf_url":"https://arxiv.org/pdf/2610.09984","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.09984","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001610526","display_name":"Samuel Gruffaz","orcid":"https://orcid.org/0009-0000-8365-7048","observed_orcids":["https://orcid.org/0009-0000-8365-7048"]},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Gruffaz, Samuel","raw_affiliation_strings":["Tampere university , Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere university , Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157933771","display_name":"Muhammad Fawad","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Fawad, Muhammad","raw_affiliation_strings":["Tampere university , Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere university , Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5157977787","display_name":"Jaakko Nevalainen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Nevalainen, Jaakko","raw_affiliation_strings":["Tampere university , Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere university , Finland","institution_ids":["https://openalex.org/I166825849"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I166825849"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.8252000212669373,"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":0.8252000212669373,"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":0.10029999911785126,"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/T12676","display_name":"Machine Learning and ELM","score":0.026000000536441803,"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/extreme-value-theory","display_name":"extreme value theory","score":0.9829999804496765},{"id":"https://openalex.org/keywords/threshold-adaptation","display_name":"threshold adaptation","score":0.9350000023841858},{"id":"https://openalex.org/keywords/permutation-tests","display_name":"permutation tests","score":0.671999990940094},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.8069999814033508},{"id":"https://openalex.org/keywords/tumor-screening","display_name":"cancer screening","score":0.6880000233650208},{"id":"https://openalex.org/keywords/classifier-interpretability","display_name":"classifier interpretability","score":0.6430000066757202}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8303999900817871},{"id":"https://openalex.org/C147581598","wikidata":"https://www.wikidata.org/wiki/Q729429","display_name":"Extreme value theory","level":2,"score":0.6714000105857849},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.6075000166893005},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5745000243186951},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5475999712944031},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5069000124931335},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4722999930381775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4625000059604645},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.45989999175071716},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C39847760","wikidata":"https://www.wikidata.org/wiki/Q1385465","display_name":"Extreme point","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33809998631477356},{"id":"https://openalex.org/C169707849","wikidata":"https://www.wikidata.org/wiki/Q1617240","display_name":"Generalized extreme value distribution","level":3,"score":0.3296999931335449},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32089999318122864},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3142000138759613},{"id":"https://openalex.org/C104317236","wikidata":"https://www.wikidata.org/wiki/Q4330126","display_name":"Classification rule","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.30889999866485596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.304500013589859},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C183905921","wikidata":"https://www.wikidata.org/wiki/Q1038757","display_name":"Multiple comparisons problem","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28690001368522644},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.2800999879837036},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.09984","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.09984","pdf_url":"https://arxiv.org/pdf/2610.09984","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.09984","doi":"https://doi.org/10.48550/arxiv.2610.09984","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.09984","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.09984","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.09984","pdf_url":"https://arxiv.org/pdf/2610.09984","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7220949666.pdf","grobid_xml":"https://content.openalex.org/works/W7220949666.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W2258824649","https://openalex.org/W4288055226"],"related_works":["https://openalex.org/W3007532985","https://openalex.org/W3179825165","https://openalex.org/W2084055031","https://openalex.org/W2287243135","https://openalex.org/W4394665569","https://openalex.org/W2615517301","https://openalex.org/W2945044016","https://openalex.org/W4226316650","https://openalex.org/W2891688167","https://openalex.org/W3094220251"],"abstract_inverted_index":{"While":[0],"binary":[1],"classification":[2],"is":[3,20,48,58],"one":[4],"of":[5,67,114],"the":[6,14,18,40,46,65,71],"most":[7],"extensively":[8],"studied":[9],"problems":[10],"in":[11,16,70,86],"machine":[12],"learning,":[13],"regime":[15],"which":[17],"goal":[19],"to":[21,49,105,135],"learn":[22,50],"a":[23,51,80,95,101,136],"classifier":[24,52],"with":[25,62,94,123],"an":[26,133],"almost":[27],"zero":[28],"false":[29,54],"negative":[30,55],"rate":[31,56],"remains":[32],"largely":[33],"unexplored.":[34],"In":[35,126],"this":[36,76],"paper,":[37],"we":[38,78,128],"introduce":[39],"Extreme":[41,90],"Binary":[42],"Classification":[43],"problem,":[44,77],"where":[45],"objective":[47],"whose":[53],"$\u03b1$":[57],"constrained":[59],"by":[60],"$\u03b5_{N_1}=o_{N_1\\to\\infty}(1/N_1)$,":[61],"$N_1$":[63],"denoting":[64],"number":[66],"positive":[68],"examples":[69],"training":[72],"set.":[73],"To":[74],"address":[75],"propose":[79],"threshold":[81],"adaptation":[82],"method":[83],"theoretically":[84],"grounded":[85],"guarantees":[87],"derived":[88],"from":[89],"Value":[91],"Theory,":[92],"together":[93],"feature":[96],"selection":[97],"procedure":[98],"based":[99],"on":[100,110],"permutation":[102],"test":[103],"applied":[104],"sample":[106],"maxima.":[107],"Experimental":[108],"results":[109],"four":[111],"real-world":[112],"datasets":[113],"varying":[115],"sizes":[116],"demonstrate":[117],"that":[118],"our":[119],"approach":[120],"compares":[121],"favorably":[122],"state-of-the-art":[124],"methods.":[125],"addition,":[127],"illustrate":[129],"its":[130],"interpretability":[131],"through":[132],"application":[134],"cancer":[137],"screening":[138],"dataset.":[139]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-09T00:00:00"}
