{"id":"https://openalex.org/W1992562594","doi":"https://doi.org/10.1109/grc.2013.6740371","title":"Boosting for learning from multiclass data sets via a regularized loss function","display_name":"Boosting for learning from multiclass data sets via a regularized loss function","publication_year":2013,"publication_date":"2013-12-01","ids":{"openalex":"https://openalex.org/W1992562594","doi":"https://doi.org/10.1109/grc.2013.6740371","mag":"1992562594"},"language":"en","primary_location":{"id":"doi:10.1109/grc.2013.6740371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/grc.2013.6740371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Granular Computing (GrC)","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/A5020093775","display_name":"Mohamed Abouelenien","orcid":"https://orcid.org/0000-0001-5351-5778"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohamed Abouelenien","raw_affiliation_strings":["Department of Computer Science and Engineering, University of North Texas, Denton, TX","Department of Computer Science and Engineering University of North Texas Denton, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of North Texas, Denton, TX","institution_ids":["https://openalex.org/I123534392"]},{"raw_affiliation_string":"Department of Computer Science and Engineering University of North Texas Denton, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021355100","display_name":"Xiaohui Yuan","orcid":"https://orcid.org/0000-0001-6897-4563"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohui Yuan","raw_affiliation_strings":["Department of Computer Science and Engineering, University of North Texas, Denton, TX","Department of Computer Science and Engineering University of North Texas Denton, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of North Texas, Denton, TX","institution_ids":["https://openalex.org/I123534392"]},{"raw_affiliation_string":"Department of Computer Science and Engineering University of North Texas Denton, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020093775"],"corresponding_institution_ids":["https://openalex.org/I123534392"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.06446064,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"24","issue":null,"first_page":"4","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9986000061035156,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9986000061035156,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9986000061035156,"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":0.9975000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.8955340385437012},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.655113697052002},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6272444128990173},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.619560718536377},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.589618980884552},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5753188729286194},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5632780194282532},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5059890151023865},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26133352518081665},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1890513300895691}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8955340385437012},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.655113697052002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6272444128990173},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.619560718536377},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.589618980884552},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5753188729286194},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5632780194282532},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5059890151023865},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26133352518081665},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1890513300895691},{"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:10.1109/grc.2013.6740371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/grc.2013.6740371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Granular Computing (GrC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1522999595","https://openalex.org/W1563489020","https://openalex.org/W1968969471","https://openalex.org/W1988790447","https://openalex.org/W2032210760","https://openalex.org/W2061804201","https://openalex.org/W2067885219","https://openalex.org/W2123921160","https://openalex.org/W2124430220","https://openalex.org/W2134987394","https://openalex.org/W2140337774","https://openalex.org/W2570929519","https://openalex.org/W2912934387","https://openalex.org/W2994340921","https://openalex.org/W3120740533","https://openalex.org/W4212883601","https://openalex.org/W4244952642","https://openalex.org/W6635552349","https://openalex.org/W6678576760","https://openalex.org/W6680028700","https://openalex.org/W6680860065"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W4231274751","https://openalex.org/W1549363203","https://openalex.org/W3082059448","https://openalex.org/W4313640622","https://openalex.org/W4401552848","https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059"],"abstract_inverted_index":{"Boosting":[0],"methods":[1],"employ":[2,77],"a":[3,44,89,102],"sequence":[4],"of":[5,47,71,109,136,177],"base":[6,53,127,165],"classifiers":[7,79,111,145,166],"to":[8,21,76,149,181],"improve":[9],"accuracy.":[10],"While":[11],"successful":[12],"with":[13,62,101],"binary":[14,19,37],"classification,":[15],"the":[16,33,48,81,107,110,120,126,137,156,164,174],"conversion":[17],"from":[18,69],"boosting":[20,23,82,92],"multi-class":[22,91,114],"is":[24,147],"not":[25],"straight":[26],"forward.":[27],"The":[28,117,152],"direct":[29,90],"extension":[30,93],"avoids":[31],"converting":[32],"problem":[34],"into":[35],"multiple":[36],"problems":[38,42],"but":[39],"suffers":[40],"several":[41],"including":[43],"vague":[45],"determination":[46],"error":[49,157],"condition":[50,158],"for":[51],"each":[52,160],"classifier,":[54],"an":[55,96],"elongated":[56],"training":[57],"time":[58],"that":[59,94,105],"increases":[60],"significantly":[61],"large":[63],"datasets,":[64],"early":[65],"termination":[66],"which":[67,146],"results":[68,169],"repetition":[70],"misclassified":[72],"instances,":[73],"and":[74,112,124],"inability":[75],"stable":[78],"within":[80],"scheme.":[83],"In":[84],"this":[85],"paper,":[86],"we":[87],"introduce":[88],"combines":[95],"intelligent":[97],"class-based":[98],"stratified":[99],"sampling":[100],"regularization":[103],"parameter":[104,118],"regularizes":[106],"unpredictability":[108],"accommodates":[113],"data":[115],"sets.":[116],"extends":[119],"exponential":[121],"loss":[122],"function":[123],"penalizes":[125],"classifier":[128],"when":[129],"it":[130],"favors":[131],"specific":[132],"instances":[133],"on":[134,163,170],"expense":[135],"correct":[138],"distribution.":[139],"This":[140],"methodology":[141],"ensures":[142],"diversity":[143],"among":[144],"vital":[148],"ensemble":[150],"learning.":[151],"proposed":[153],"method":[154,179],"alters":[155],"at":[159],"iteration":[161],"based":[162],"performance.":[167],"Experimental":[168],"different":[171],"applications":[172],"demonstrate":[173],"superior":[175],"performance":[176],"our":[178],"compared":[180],"state-of-the-art":[182],"methods.":[183]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
