{"id":"https://openalex.org/W2001127259","doi":"https://doi.org/10.1145/1102351.1102397","title":"A smoothed boosting algorithm using probabilistic output codes","display_name":"A smoothed boosting algorithm using probabilistic output codes","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W2001127259","doi":"https://doi.org/10.1145/1102351.1102397","mag":"2001127259"},"language":"en","primary_location":{"id":"doi:10.1145/1102351.1102397","doi":"https://doi.org/10.1145/1102351.1102397","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1102351.1102397","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd international conference on Machine learning - ICML '05","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5069394608","display_name":"Rong Jin","orcid":"https://orcid.org/0000-0002-8797-4646","observed_orcids":["https://orcid.org/0000-0002-8797-4646","https://orcid.org/0000-0003-1789-236X"]},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rong Jin","raw_affiliation_strings":["Michigan State University, MI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Michigan State University, MI","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100409994","display_name":"Jian Zhang","orcid":"https://orcid.org/0000-0002-7240-3541","observed_orcids":["https://orcid.org/0000-0002-7240-3541","https://orcid.org/0000-0001-8316-1894","https://orcid.org/0009-0008-9576-4930","https://orcid.org/0000-0002-9086-7622","https://orcid.org/0009-0004-9281-8869","https://orcid.org/0000-0003-3364-1544"]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Zhang","raw_affiliation_strings":["Carnegie Mellon University, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, PA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2501,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.40710253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"361","last_page":"368"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.5332000255584717,"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":0.5332000255584717,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.3698999881744385,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.03909999877214432,"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","score":0.6480000019073486},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"multi-class classification","score":0.7720000147819519},{"id":"https://openalex.org/keywords/error-correcting-output-codes","display_name":"error-correcting output codes","score":0.859000027179718},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.9779999852180481},{"id":"https://openalex.org/keywords/binary-classifiers","display_name":"binary classifiers","score":0.5080000162124634}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8865392208099365},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6697078943252563},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6161507368087769},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6007271409034729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5989363789558411},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5730189085006714},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5551641583442688},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49517980217933655},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4640817642211914},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.45709413290023804},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4010242223739624},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22363045811653137},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2032896876335144},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0874597430229187},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/1102351.1102397","doi":"https://doi.org/10.1145/1102351.1102397","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1102351.1102397","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd international conference on Machine learning - ICML '05","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.59.6774","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.6774","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~jianzhan/./papers/icml05jin.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.82.212","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.82.212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://imls.engr.oregonstate.edu/www/htdocs/proceedings/icml2005/papers/046_ASmoothed_JinZhang.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W44402286","https://openalex.org/W1525410796","https://openalex.org/W1532883599","https://openalex.org/W1537489869","https://openalex.org/W1540007258","https://openalex.org/W1578772208","https://openalex.org/W1605688901","https://openalex.org/W1676820704","https://openalex.org/W1988790447","https://openalex.org/W2099968818","https://openalex.org/W2112076978","https://openalex.org/W2152761983","https://openalex.org/W2157112198","https://openalex.org/W2293879901"],"related_works":["https://openalex.org/W1997079092","https://openalex.org/W1578772208","https://openalex.org/W1966045914","https://openalex.org/W7096860067","https://openalex.org/W7100436396","https://openalex.org/W2115857118","https://openalex.org/W7095757201","https://openalex.org/W1974509219","https://openalex.org/W7098811341","https://openalex.org/W2134987394"],"abstract_inverted_index":{"AdaBoost.OC":[0,59,127],"has":[1],"shown":[2],"to":[3,25,43,74,92],"be":[4,104],"an":[5],"effective":[6,124],"method":[7,24],"in":[8,61],"boosting":[9,54,72,118],"\"weak\"":[10],"binary":[11,35,94],"classifiers":[12],"for":[13,96,129],"multi-class":[14,28,130],"learning.":[15,131],"It":[16,65,86],"employs":[17],"the":[18,40,58,71,76,81,116,126],"Error":[19],"Correcting":[20],"Output":[21],"Code":[22],"(ECOC)":[23],"convert":[26],"a":[27,32,52,67,88],"learning":[29],"problem":[30,79],"into":[31,70],"set":[33],"of":[34],"classification":[36],"problems,":[37],"and":[38,84,123],"applies":[39],"AdaBoost":[41,82],"algorithm":[42,55,60,73,119,128],"solve":[44],"them":[45],"efficiently.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50],"propose":[51],"new":[53],"that":[56,100,115],"improves":[57],"two":[62],"aspects:":[63],"1)":[64],"introduces":[66,87],"smoothing":[68],"mechanism":[69],"alleviate":[75],"potential":[77],"overfitting":[78],"with":[80,109],"algorithm,":[83],"2)":[85],"probabilistic":[89],"coding":[90],"scheme":[91],"generate":[93],"codes":[95],"multiple":[97],"classes":[98],"such":[99],"training":[101],"errors":[102],"can":[103],"efficiently":[105],"reduced.":[106],"Empirical":[107],"studies":[108],"seven":[110],"UCI":[111],"datasets":[112],"have":[113],"indicated":[114],"proposed":[117],"is":[120],"more":[121],"robust":[122],"than":[125]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2016-06-24T00:00:00"}
