{"id":"https://openalex.org/W2730916893","doi":"https://doi.org/10.1109/tnnls.2017.2711028","title":"Robust and Efficient Boosting Method Using the Conditional Risk","display_name":"Robust and Efficient Boosting Method Using the Conditional Risk","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2730916893","doi":"https://doi.org/10.1109/tnnls.2017.2711028","mag":"2730916893","pmid":"https://pubmed.ncbi.nlm.nih.gov/28678719"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2017.2711028","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2017.2711028","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1806.08151","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005930431","display_name":"Zhi Xiao","orcid":"https://orcid.org/0000-0003-2897-888X"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Xiao","raw_affiliation_strings":["Department of Information Management, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Management, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100631897","display_name":"Zhe Luo","orcid":"https://orcid.org/0000-0002-8127-7356"},"institutions":[{"id":"https://openalex.org/I917184967","display_name":"Bank of China","ror":"https://ror.org/02mt4s337","country_code":"CN","type":"other","lineage":["https://openalex.org/I917184967"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Luo","raw_affiliation_strings":["Bank of China, Nanning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bank of China, Nanning, China","institution_ids":["https://openalex.org/I917184967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056733015","display_name":"Bo Zhong","orcid":"https://orcid.org/0000-0002-3128-9914"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhong","raw_affiliation_strings":["Department of Statistics and Actuarial Science, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Actuarial Science, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070402968","display_name":"Xin Dang","orcid":"https://orcid.org/0000-0002-3372-3825"},"institutions":[{"id":"https://openalex.org/I368840534","display_name":"University of Mississippi","ror":"https://ror.org/02teq1165","country_code":"US","type":"education","lineage":["https://openalex.org/I368840534","https://openalex.org/I4210141039"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Dang","raw_affiliation_strings":["Department of Mathematics, University of Mississippi, Oxford, MS, USA"],"raw_orcid":"https://orcid.org/0000-0002-3372-3825","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Mississippi, Oxford, MS, USA","institution_ids":["https://openalex.org/I368840534"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6523,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.88052778,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"29","issue":"7","first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998000264167786,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9998000264167786,"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/T10057","display_name":"Face and Expression Recognition","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9955000281333923,"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/boosting","display_name":"Boosting (machine learning)","score":0.7086554169654846},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6894350647926331},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6735268831253052},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6452076435089111},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6295682191848755},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5603865385055542},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5402121543884277},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.454702228307724},{"id":"https://openalex.org/keywords/trustworthiness","display_name":"Trustworthiness","score":0.44064414501190186},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4170915484428406},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41421419382095337},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3388567566871643},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08638548851013184}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7086554169654846},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6894350647926331},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6735268831253052},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6452076435089111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6295682191848755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5603865385055542},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5402121543884277},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.454702228307724},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.44064414501190186},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4170915484428406},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41421419382095337},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3388567566871643},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08638548851013184},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tnnls.2017.2711028","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2017.2711028","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:28678719","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/28678719","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"pmh:oai:arXiv.org:1806.08151","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.08151","pdf_url":"https://arxiv.org/pdf/1806.08151","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1806.08151","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.08151","pdf_url":"https://arxiv.org/pdf/1806.08151","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W123339444","https://openalex.org/W1480376833","https://openalex.org/W1487831638","https://openalex.org/W1563626982","https://openalex.org/W1565746575","https://openalex.org/W1570592793","https://openalex.org/W1579119141","https://openalex.org/W1585680390","https://openalex.org/W1591657322","https://openalex.org/W1605688901","https://openalex.org/W1666146316","https://openalex.org/W1666942233","https://openalex.org/W1670346950","https://openalex.org/W1698155719","https://openalex.org/W1926592634","https://openalex.org/W1968032964","https://openalex.org/W1982032418","https://openalex.org/W1985400179","https://openalex.org/W1988790447","https://openalex.org/W1988950592","https://openalex.org/W1994807229","https://openalex.org/W1997731668","https://openalex.org/W2000459372","https://openalex.org/W2009045943","https://openalex.org/W2011143997","https://openalex.org/W2016717446","https://openalex.org/W2022479766","https://openalex.org/W2024046085","https://openalex.org/W2032210760","https://openalex.org/W2037995333","https://openalex.org/W2051570664","https://openalex.org/W2057778513","https://openalex.org/W2059507684","https://openalex.org/W2067407532","https://openalex.org/W2068221105","https://openalex.org/W2070350843","https://openalex.org/W2081410858","https://openalex.org/W2092260586","https://openalex.org/W2099854653","https://openalex.org/W2102281237","https://openalex.org/W2105231256","https://openalex.org/W2112076978","https://openalex.org/W2120396754","https://openalex.org/W2124104513","https://openalex.org/W2128808713","https://openalex.org/W2142182456","https://openalex.org/W2162139029","https://openalex.org/W2168118654","https://openalex.org/W2172059508","https://openalex.org/W3098897816","https://openalex.org/W3100570787","https://openalex.org/W3120740533","https://openalex.org/W3141455185","https://openalex.org/W4234721591","https://openalex.org/W4244952642","https://openalex.org/W4285719527","https://openalex.org/W4297944103","https://openalex.org/W6605090765","https://openalex.org/W6634164890","https://openalex.org/W6635381760","https://openalex.org/W6675256168","https://openalex.org/W6676769703","https://openalex.org/W6678507914"],"related_works":["https://openalex.org/W2393964553","https://openalex.org/W2327035729","https://openalex.org/W2348748958","https://openalex.org/W3039673966","https://openalex.org/W1538046993","https://openalex.org/W2884325279","https://openalex.org/W1570592793","https://openalex.org/W1525436954","https://openalex.org/W2385662756","https://openalex.org/W2585372724"],"abstract_inverted_index":{"Well":[0],"known":[1],"for":[2],"its":[3],"simplicity":[4],"and":[5,87,131,156,164],"effectiveness":[6],"in":[7,29,153],"classification,":[8],"AdaBoost,":[9],"however,":[10],"suffers":[11],"from":[12,135],"overfitting":[13],"when":[14,107,158],"class-conditional":[15],"distributions":[16],"have":[17,94],"significant":[18],"overlap.":[19],"Moreover,":[20],"it":[21,58,74],"is":[22,59,99,109],"very":[23],"sensitive":[24],"to":[25,61,93,101],"noise":[26],"that":[27,98,102],"appears":[28],"the":[30,35,46,53,83,89,104,120,146,150,161],"labels.":[31],"This":[32],"paper":[33],"tackles":[34],"above":[36],"limitations":[37],"simultaneously":[38],"via":[39,82],"optimizing":[40],"a":[41,76,110],"modified":[42],"loss":[43],"function":[44],"(i.e.,":[45],"conditional":[47,115],"risk).":[48],"The":[49,142],"proposed":[50,121,151],"approach":[51],"has":[52],"following":[54],"two":[55],"advantages.":[56],"First,":[57],"able":[60],"directly":[62],"take":[63],"into":[64],"account":[65],"label":[66,71],"uncertainty":[67],"with":[68,160],"an":[69],"associated":[70],"confidence.":[72],"Second,":[73],"introduces":[75],"trustworthiness":[77],"measure":[78],"on":[79],"training":[80],"samples":[81],"Bayesian":[84],"risk":[85],"rule,":[86],"hence":[88],"resulting":[90],"classifier":[91],"tends":[92],"finite":[95],"sample":[96],"performance":[97],"superior":[100],"of":[103,119,149],"original":[105,162],"AdaBoost":[106,163,168],"there":[108],"large":[111],"overlap":[112],"between":[113],"class":[114],"distributions.":[116],"Theoretical":[117],"properties":[118],"method":[122,152],"are":[123,140],"investigated.":[124],"Extensive":[125],"experimental":[126],"results":[127],"using":[128],"synthetic":[129],"data":[130,133],"real-world":[132],"sets":[134],"UCI":[136],"machine":[137],"learning":[138],"repository":[139],"provided.":[141],"empirical":[143],"study":[144],"shows":[145],"high":[147],"competitiveness":[148],"predication":[154],"accuracy":[155],"robustness":[157],"compared":[159],"several":[165],"existing":[166],"robust":[167],"algorithms.":[169]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}