{"id":"https://openalex.org/W2184928272","doi":"https://doi.org/10.1109/dsaa.2015.7344791","title":"Label noise correction methods","display_name":"Label noise correction methods","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2184928272","doi":"https://doi.org/10.1109/dsaa.2015.7344791","mag":"2184928272"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa.2015.7344791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344791","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-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/A5003119236","display_name":"Bryce Nicholson","orcid":null},"institutions":[{"id":"https://openalex.org/I32038505","display_name":"University of Central Arkansas","ror":"https://ror.org/029bp0k25","country_code":"US","type":"education","lineage":["https://openalex.org/I32038505"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bryce Nicholson","raw_affiliation_strings":["Computer Science Department, University of Central Arkansas"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Central Arkansas","institution_ids":["https://openalex.org/I32038505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101681013","display_name":"Jing Zhang","orcid":"https://orcid.org/0000-0001-8130-2729"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051706630","display_name":"Victor S. Sheng","orcid":"https://orcid.org/0000-0003-4960-174X"},"institutions":[{"id":"https://openalex.org/I32038505","display_name":"University of Central Arkansas","ror":"https://ror.org/029bp0k25","country_code":"US","type":"education","lineage":["https://openalex.org/I32038505"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Victor S. Sheng","raw_affiliation_strings":["Computer Science Department, University of Central Arkansas"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Central Arkansas","institution_ids":["https://openalex.org/I32038505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100655747","display_name":"Zhiheng Wang","orcid":"https://orcid.org/0000-0002-6880-9485"},"institutions":[{"id":"https://openalex.org/I2799961584","display_name":"China Executive Leadership Academy Pudong","ror":"https://ror.org/02t573g91","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799961584"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiheng Wang","raw_affiliation_strings":["IT Department, China Executive Leadership Academy Pudong, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"IT Department, China Executive Leadership Academy Pudong, Shanghai, China","institution_ids":["https://openalex.org/I2799961584"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5003119236"],"corresponding_institution_ids":["https://openalex.org/I32038505"],"apc_list":null,"apc_paid":null,"fwci":3.883,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.94188774,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9994000196456909,"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":0.9994000196456909,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9889000058174133,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9864000082015991,"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/computer-science","display_name":"Computer science","score":0.7731608152389526},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.690986692905426},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6421520113945007},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.599560022354126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5903491377830505},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5017139911651611},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48834869265556335},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4596618413925171},{"id":"https://openalex.org/keywords/silence","display_name":"Silence","score":0.4525264799594879},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4335617423057556},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4277692437171936},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4228055477142334},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3991170823574066},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33967146277427673},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2407723367214203},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.21615901589393616},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1747208535671234}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731608152389526},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.690986692905426},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6421520113945007},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.599560022354126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5903491377830505},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5017139911651611},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48834869265556335},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4596618413925171},{"id":"https://openalex.org/C2781115785","wikidata":"https://www.wikidata.org/wiki/Q502261","display_name":"Silence","level":2,"score":0.4525264799594879},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4335617423057556},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4277692437171936},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4228055477142334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3991170823574066},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33967146277427673},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2407723367214203},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.21615901589393616},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1747208535671234},{"id":"https://openalex.org/C107038049","wikidata":"https://www.wikidata.org/wiki/Q35986","display_name":"Aesthetics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa.2015.7344791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344791","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W41502859","https://openalex.org/W877541198","https://openalex.org/W1564764609","https://openalex.org/W1605688901","https://openalex.org/W1992419399","https://openalex.org/W2012243161","https://openalex.org/W2022479766","https://openalex.org/W2096035548","https://openalex.org/W2098370488","https://openalex.org/W2113290770","https://openalex.org/W2125943921","https://openalex.org/W2133990480","https://openalex.org/W2140405352","https://openalex.org/W2153233077","https://openalex.org/W2164873210","https://openalex.org/W2167460663","https://openalex.org/W2810995976","https://openalex.org/W2908598920","https://openalex.org/W6601696430","https://openalex.org/W6623811885","https://openalex.org/W6633899822","https://openalex.org/W6677082149"],"related_works":["https://openalex.org/W2140535326","https://openalex.org/W1607271848","https://openalex.org/W4251121070","https://openalex.org/W2734809835","https://openalex.org/W2316014825","https://openalex.org/W2184961913","https://openalex.org/W1965764303","https://openalex.org/W2214987084","https://openalex.org/W1964726586","https://openalex.org/W3008391423"],"abstract_inverted_index":{"The":[0,12],"important":[1],"task":[2],"of":[3,14,86,92,105,135],"correcting":[4],"label":[5,18,26,38,128,150],"noise":[6],"is":[7,51,177],"addressed":[8],"infrequently":[9],"in":[10,165],"literature.":[11],"difficulty":[13],"developing":[15],"a":[16,52,76],"robust":[17],"correction":[19],"algorithm":[20,72],"leads":[21],"to":[22,36,43,59,88],"this":[23],"silence":[24],"concerning":[25],"correction.":[27],"To":[28],"break":[29],"the":[30,90,127,178],"silence,":[31],"we":[32],"propose":[33],"two":[34],"algorithms":[35],"correct":[37],"noise.":[39],"One":[40],"utilizes":[41],"self-training":[42],"re-label":[44],"noise,":[45],"called":[46,64,79],"Self-Training":[47],"Correction":[48,66,175],"(STC).":[49],"Another":[50],"clustering-based":[53],"method,":[54],"which":[55],"groups":[56],"instances":[57],"together":[58],"infer":[60],"their":[61,124],"ground-truth":[62],"labels,":[63],"Cluster-based":[65,174],"(CC).":[67],"We":[68,96,113],"also":[69],"adapt":[70],"an":[71,84],"from":[73],"previous":[74],"work,":[75],"consensus-based":[77],"method":[78,176],"Polishing":[80,98,110,120],"that":[81,100,144],"consults":[82],"with":[83,119],"ensemble":[85],"classifiers":[87],"change":[89],"values":[91],"attributes":[93],"and":[94,107,132,137,141,154,159],"labels.":[95],"simplify":[97],"such":[99],"it":[101,109],"only":[102,145],"alters":[103],"labels":[104],"instances,":[106],"call":[108],"Labels":[111,121],"(PL).":[112],"experimentally":[114],"compare":[115],"our":[116,173],"novel":[117],"methods":[118],"by":[122],"examining":[123],"improvements":[125],"on":[126],"qualities,":[129,131,151,153],"model":[130,152],"AUC":[133,155],"metrics":[134,156,164],"binary":[136],"multi-class":[138],"data":[139],"sets,":[140],"ultimately":[142],"conclude":[143],"CC":[146],"can":[147,161],"significantly":[148],"improve":[149,162],"consistently.":[157],"STC":[158],"PL":[160],"these":[163],"some":[166],"cases,":[167],"but":[168],"not":[169],"as":[170],"reliably.":[171],"Hence,":[172],"best.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}