{"id":"https://openalex.org/W3118622230","doi":"https://doi.org/10.1080/00401706.2020.1870564","title":"Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data","display_name":"Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data","publication_year":2021,"publication_date":"2021-01-04","ids":{"openalex":"https://openalex.org/W3118622230","doi":"https://doi.org/10.1080/00401706.2020.1870564","mag":"3118622230"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2020.1870564","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2020.1870564","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"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/A5103034149","display_name":"Seokho Lee","orcid":"https://orcid.org/0000-0001-6423-519X"},"institutions":[{"id":"https://openalex.org/I83436808","display_name":"Hankuk University of Foreign Studies","ror":"https://ror.org/051q2m369","country_code":"KR","type":"education","lineage":["https://openalex.org/I83436808"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seokho Lee","raw_affiliation_strings":["Department of Statistics, Hankuk University of Foreign Studies, Yongin, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Hankuk University of Foreign Studies, Yongin, Republic of Korea","institution_ids":["https://openalex.org/I83436808"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086455216","display_name":"Hyelim Jung","orcid":null},"institutions":[{"id":"https://openalex.org/I83436808","display_name":"Hankuk University of Foreign Studies","ror":"https://ror.org/051q2m369","country_code":"KR","type":"education","lineage":["https://openalex.org/I83436808"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyelim Jung","raw_affiliation_strings":["Department of Statistics, Hankuk University of Foreign Studies, Yongin, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Hankuk University of Foreign Studies, Yongin, Republic of Korea","institution_ids":["https://openalex.org/I83436808"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103034149"],"corresponding_institution_ids":["https://openalex.org/I83436808"],"apc_list":null,"apc_paid":null,"fwci":0.4079,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66374891,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"64","issue":"1","first_page":"18","last_page":"29"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9980999827384949,"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":0.9980999827384949,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.968500018119812,"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/logistic-regression","display_name":"Logistic regression","score":0.6370934247970581},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.6179312467575073},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5947108268737793},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5913598537445068},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5453691482543945},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5362569093704224},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5240174531936646},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5027661323547363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4628962576389313},{"id":"https://openalex.org/keywords/logistic-model-tree","display_name":"Logistic model tree","score":0.4542132616043091},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44742846488952637},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4281575083732605},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.39731985330581665},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3367658853530884},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.149370014667511}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6370934247970581},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.6179312467575073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5947108268737793},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5913598537445068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5453691482543945},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5362569093704224},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5240174531936646},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5027661323547363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4628962576389313},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.4542132616043091},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44742846488952637},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4281575083732605},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39731985330581665},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3367658853530884},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.149370014667511},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/00401706.2020.1870564","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2020.1870564","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W72116547","https://openalex.org/W119127125","https://openalex.org/W1973650202","https://openalex.org/W1978153422","https://openalex.org/W1982032418","https://openalex.org/W1997987234","https://openalex.org/W2000818163","https://openalex.org/W2004485888","https://openalex.org/W2020926105","https://openalex.org/W2022465039","https://openalex.org/W2030843733","https://openalex.org/W2039151281","https://openalex.org/W2043182541","https://openalex.org/W2077452329","https://openalex.org/W2081305621","https://openalex.org/W2089394015","https://openalex.org/W2102348129","https://openalex.org/W2122504634","https://openalex.org/W2143030432","https://openalex.org/W2167460663","https://openalex.org/W2285776716","https://openalex.org/W2298091009","https://openalex.org/W2981446616","https://openalex.org/W3100971749","https://openalex.org/W4255230573"],"related_works":["https://openalex.org/W2792520941","https://openalex.org/W2033000528","https://openalex.org/W2563096758","https://openalex.org/W2750047195","https://openalex.org/W4386053843","https://openalex.org/W3158004940","https://openalex.org/W3134209864","https://openalex.org/W2907454910","https://openalex.org/W4289097367","https://openalex.org/W2951930671"],"abstract_inverted_index":{"We":[0,57,102,115,124],"consider":[1],"a":[2,31,37,93,99],"binary":[3],"classification":[4,27],"problem":[5],"in":[6,12,89,98],"the":[7,13,20,72,77,108,126],"case":[8],"where":[9],"some":[10],"observations":[11],"training":[14,55],"data":[15],"are":[16,64],"incorrectly":[17],"labeled.":[18],"In":[19,39],"presence":[21],"of":[22,79],"such":[23],"label":[24,44,61,67,84,95],"noise,":[25],"conventional":[26],"fails":[28],"to":[29,33,36],"obtain":[30],"classifier":[32],"be":[34],"generalized":[35],"population.":[38],"this":[40],"work,":[41],"we":[42,91],"investigate":[43],"noise":[45,68,85,96],"logistic":[46,69],"regression":[47,70],"and":[48,75,121],"explain":[49],"how":[50],"it":[51],"works":[52],"with":[53],"noisy":[54],"data.":[56],"demonstrate":[58,116],"that,":[59],"when":[60],"transition":[62,129],"probabilities":[63],"correctly":[65],"provided,":[66],"satisfies":[71],"Fisher":[73],"consistency":[74],"enjoys":[76],"property":[78],"robustness.":[80],"To":[81],"accommodate":[82],"various":[83],"mechanisms":[86],"that":[87],"occur":[88],"practice,":[90],"propose":[92,103],"flexible":[94,128],"model":[97,130],"nonparametric":[100],"way.":[101],"an":[104],"efficient":[105],"algorithm":[106],"under":[107,119],"thresholding":[109],"rule":[110],"for":[111,134],"individual":[112],"parameter":[113],"estimation.":[114],"its":[117],"performance":[118],"synthetic":[120],"real":[122],"examples.":[123],"discuss":[125],"proposed":[127],"is":[131],"also":[132],"useful":[133],"robust":[135],"classification.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}