{"id":"https://openalex.org/W2170238270","doi":"https://doi.org/10.1109/cvpr.2008.4587347","title":"Mining compositional features for boosting","display_name":"Mining compositional features for boosting","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2170238270","doi":"https://doi.org/10.1109/cvpr.2008.4587347","mag":"2170238270"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587347","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587347","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","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/A5085245110","display_name":"Junsong Yuan","orcid":"https://orcid.org/0000-0002-7901-8793"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Junsong Yuan","raw_affiliation_strings":["EECS Department, Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"EECS Department, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055469774","display_name":"Jiebo Luo","orcid":"https://orcid.org/0000-0002-4516-9729"},"institutions":[{"id":"https://openalex.org/I4210159451","display_name":"Kodak (United States)","ror":"https://ror.org/04rn3ph18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210159451"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiebo Luo","raw_affiliation_strings":["Kodak Research Laboratories, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Kodak Research Laboratories, Rochester, NY, USA","institution_ids":["https://openalex.org/I4210159451"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002074693","display_name":"Ying Wu","orcid":"https://orcid.org/0000-0002-4095-8315"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Wu","raw_affiliation_strings":["EECS Department, Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"EECS Department, Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5085245110"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":20.992,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.99218769,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9939000010490417,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.929572582244873},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.816585123538971},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6748773455619812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6735817790031433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.648867130279541},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.634604811668396},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5798000693321228},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.49306222796440125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46880701184272766},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4686177372932434},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43640202283859253},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4269673228263855}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.929572582244873},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.816585123538971},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6748773455619812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6735817790031433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.648867130279541},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.634604811668396},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5798000693321228},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.49306222796440125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46880701184272766},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4686177372932434},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43640202283859253},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4269673228263855}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2008.4587347","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587347","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.160.5630","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.5630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ece.northwestern.edu/~yingwu/papers/conference/2008/CVPR2008c_Mining_final.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.409.4773","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.409.4773","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mplab.ucsd.edu/wp-content/uploads/cvpr2008/conference/data/papers/007.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6600000262260437}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1678356000","https://openalex.org/W1968969471","https://openalex.org/W2024046085","https://openalex.org/W2099258729","https://openalex.org/W2110893883","https://openalex.org/W2114758632","https://openalex.org/W2117169652","https://openalex.org/W2119996767","https://openalex.org/W2129763352","https://openalex.org/W2137454801","https://openalex.org/W2147625498","https://openalex.org/W2148596671","https://openalex.org/W2151953639","https://openalex.org/W2152066136","https://openalex.org/W2155979701","https://openalex.org/W2159680539","https://openalex.org/W2162833766","https://openalex.org/W2162915993","https://openalex.org/W2167776024","https://openalex.org/W2170146448","https://openalex.org/W3097096317","https://openalex.org/W6677171743","https://openalex.org/W6683874248","https://openalex.org/W7062670285"],"related_works":["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","https://openalex.org/W2241444561"],"abstract_inverted_index":{"The":[0],"selection":[1],"of":[2,10,49,84,97,115,133,170],"weak":[3,14,38,51,79,138],"classifiers":[4,15,139],"is":[5],"critical":[6],"to":[7,62,127,167,178],"the":[8,27,31,37,43,47,50,88,98,112],"success":[9],"boosting":[11,32,162,180],"techniques.":[12],"Poor":[13],"do":[16],"not":[17],"perform":[18],"better":[19],"than":[20,46],"random":[21],"guess,":[22],"thus":[23],"cannot":[24],"help":[25],"decrease":[26,169],"training":[28,107,171],"error":[29,172],"during":[30],"process.":[33],"Therefore,":[34],"when":[35],"constructing":[36],"classifier":[39],"pool,":[40],"we":[41,56,119],"prefer":[42],"quality":[44],"rather":[45],"quantity":[48],"classifiers.":[52],"In":[53],"this":[54],"paper,":[55],"present":[57],"a":[58,67,144,154],"data":[59,121],"mining-driven":[60],"approach":[61],"discovering":[63,116],"compositional":[64,90,117,131,163],"features":[65,77,91,132,164],"from":[66],"given":[68],"and":[69,100,173],"possibly":[70],"small":[71],"feature":[72],"pool.":[73],"Compared":[74],"with":[75,111],"individual":[76],"(e.g.":[78],"decision":[80,181],"stumps)":[81],"which":[82],"are":[83,140],"limited":[85],"discriminative":[86,101],"ability,":[87],"mined":[89],"have":[92],"guaranteed":[93],"power":[94],"in":[95],"terms":[96],"descriptive":[99],"abilities,":[102],"as":[103,105],"well":[104],"bounded":[106],"error.":[108],"To":[109],"cope":[110],"combinatorial":[113],"cost":[114],"features,":[118],"apply":[120],"mining":[122],"methods":[123],"(frequent":[124],"itemset":[125],"mining)":[126],"efficiently":[128],"find":[129],"qualified":[130],"any":[134],"possible":[135],"order.":[136],"These":[137],"further":[141],"combined":[142],"through":[143],"multi-class":[145,150],"AdaBoost":[146],"method":[147],"for":[148],"final":[149],"classification.":[151],"Experiments":[152],"on":[153],"challenging":[155],"10-class":[156],"event":[157],"recognition":[158],"problem":[159],"show":[160],"that":[161],"can":[165],"lead":[166],"faster":[168],"significantly":[174],"higher":[175],"accuracy":[176],"compared":[177],"conventional":[179],"stumps.":[182]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}