{"id":"https://openalex.org/W2158707174","doi":"https://doi.org/10.5555/1756006.1756011","title":"Classification Methods with Reject Option Based on Convex Risk Minimization","display_name":"Classification Methods with Reject Option Based on Convex Risk Minimization","publication_year":2010,"publication_date":"2010-03-01","ids":{"openalex":"https://openalex.org/W2158707174","doi":"https://doi.org/10.5555/1756006.1756011","mag":"2158707174"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.407.3926","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.3926","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume11/yuan10a/yuan10a.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5070196780","display_name":"Ming Yuan","orcid":"https://orcid.org/0000-0002-4415-8606","observed_orcids":["https://orcid.org/0000-0002-4415-8606"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Yuan","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4415-8606","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5057473211","display_name":"Marten H. Wegkamp","orcid":"https://orcid.org/0000-0001-5924-2459","observed_orcids":["https://orcid.org/0000-0001-5924-2459"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marten Wegkamp","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5924-2459","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.1343,"has_fulltext":false,"cited_by_count":97,"citation_normalized_percentile":{"value":0.9542827,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"11","issue":"5","first_page":"111","last_page":"130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.46480000019073486,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.46480000019073486,"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":0.36890000104904175,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.05009999871253967,"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/binary-classification","display_name":"binary classification","score":0.9490000009536743},{"id":"https://openalex.org/keywords/surrogate-loss-functions","display_name":"surrogate loss functions","score":0.9649999737739563},{"id":"https://openalex.org/keywords/non-convex-loss","display_name":"non-convex loss","score":0.824999988079071},{"id":"https://openalex.org/keywords/sample-consistency","display_name":"sample consistency","score":0.8619999885559082},{"id":"https://openalex.org/keywords/excess-risk-bounds","display_name":"excess risk bounds","score":0.9490000009536743},{"id":"https://openalex.org/keywords/margin-condition","display_name":"margin condition","score":0.753000020980835},{"id":"https://openalex.org/keywords/misclassification-cost","display_name":"misclassification cost","score":0.7670000195503235}],"concepts":[{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.714521050453186},{"id":"https://openalex.org/C107321475","wikidata":"https://www.wikidata.org/wiki/Q5374254","display_name":"Empirical risk minimization","level":2,"score":0.695400059223175},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6048313975334167},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.584624171257019},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.5629321932792664},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5591416954994202},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5135176777839661},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5018713474273682},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4551520347595215},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4510832726955414},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.4370115101337433},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.43674543499946594},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40367957949638367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18198105692863464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14543211460113525},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.14361506700515747},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.09979778528213501},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.407.3926","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.3926","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.org/papers/volume11/yuan10a/yuan10a.pdf","raw_type":"text"},{"id":"mag:2158707174","doi":null,"is_oa":false,"landing_page_url":"http://jmlr.csail.mit.edu/papers/volume11/yuan10a/yuan10a.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-a","cwts-core","dongbi-a","jufo-3","ki-jl-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null},{"id":"mag:3016886508","doi":"https://doi.org/10.5555/1756006.1756011","is_oa":false,"landing_page_url":"https://dl.acm.org/doi/10.5555/1756006.1756011","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-a","cwts-core","dongbi-a","jufo-3","ki-jl-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1585021961","https://openalex.org/W1982032418","https://openalex.org/W2006891751","https://openalex.org/W2007527993","https://openalex.org/W2023163512","https://openalex.org/W2024046085","https://openalex.org/W2043919728","https://openalex.org/W2049393399","https://openalex.org/W2117812871","https://openalex.org/W2126022166","https://openalex.org/W2161813894"],"related_works":["https://openalex.org/W2161813894","https://openalex.org/W1982032418","https://openalex.org/W2126022166","https://openalex.org/W2023163512","https://openalex.org/W7099321673","https://openalex.org/W7095252293","https://openalex.org/W2098158197","https://openalex.org/W2111040334","https://openalex.org/W2407344505","https://openalex.org/W2785626254"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,78],"investigate":[4],"the":[5,19,34,48,72,81,88,108],"problem":[6],"of":[7,21,31,107],"binary":[8],"classification":[9],"with":[10],"a":[11,26,101],"reject":[12],"option":[13],"in":[14],"which":[15],"one":[16],"can":[17,84,97],"withhold":[18],"decision":[20],"classifying":[22],"an":[23],"observation":[24],"at":[25],"cost":[27],"lower":[28],"than":[29],"that":[30,41,80],"misclassification.":[32],"Since":[33],"natural":[35],"loss":[36,58,117],"function":[37],"is":[38,75,110],"non-convex":[39],"so":[40],"empirical":[42],"risk":[43,83,91],"minimization":[44],"easily":[45],"becomes":[46],"infeasible,":[47],"paper":[49],"proposes":[50],"minimizing":[51],"convex":[52,57],"risks":[53,70],"based":[54],"on":[55,112],"surrogate":[56,90,116],"functions.":[59,118],"A":[60],"necessary":[61],"and":[62],"sufficient":[63],"condition":[64],"for":[65],"infinite":[66],"sample":[67],"consistency":[68],"(both":[69],"share":[71],"same":[73],"minimizer)":[74],"provided.":[76],"Moreover,":[77],"show":[79],"excess":[82,89],"be":[85,98],"bounded":[86],"through":[87],"under":[92],"appropriate":[93],"conditions.":[94],"These":[95],"bounds":[96],"tightened":[99],"by":[100],"generalized":[102],"margin":[103],"condition.":[104],"The":[105],"impact":[106],"results":[109],"illustrated":[111],"several":[113],"commonly":[114],"used":[115]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":6}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}