{"id":"https://openalex.org/W3214640948","doi":"https://doi.org/10.1109/iecon48115.2021.9589181","title":"Generating Pool of Classifiers with Hyper-Parameter Optimization for Ensemble","display_name":"Generating Pool of Classifiers with Hyper-Parameter Optimization for Ensemble","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W3214640948","doi":"https://doi.org/10.1109/iecon48115.2021.9589181","mag":"3214640948"},"language":"en","primary_location":{"id":"doi:10.1109/iecon48115.2021.9589181","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon48115.2021.9589181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2021 \u2013 47th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100411690","display_name":"Qiushi Wang","orcid":"https://orcid.org/0000-0001-7678-6941"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I4210091207","display_name":"Singapore Institute of Manufacturing Technology","ror":"https://ror.org/00f44np30","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I4210091207","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Qiushi Wang","raw_affiliation_strings":["Singapore Institute of Manufacturing Technology (SIMTech) A*STAR, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore Institute of Manufacturing Technology (SIMTech) A*STAR, Singapore","institution_ids":["https://openalex.org/I115228651","https://openalex.org/I4210091207"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108760111","display_name":"Hian-Leng Chan","orcid":null},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I4210091207","display_name":"Singapore Institute of Manufacturing Technology","ror":"https://ror.org/00f44np30","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I4210091207","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Hian-Leng Chan","raw_affiliation_strings":["Singapore Institute of Manufacturing Technology (SIMTech) A*STAR, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore Institute of Manufacturing Technology (SIMTech) A*STAR, Singapore","institution_ids":["https://openalex.org/I115228651","https://openalex.org/I4210091207"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1465,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42663071,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"15","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9987000226974487,"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":0.9987000226974487,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.996999979019165,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.994700014591217,"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/random-subspace-method","display_name":"Random subspace method","score":0.7901877164840698},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.7110961675643921},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7051130533218384},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6922483444213867},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6889219284057617},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6324832439422607},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5645354986190796},{"id":"https://openalex.org/keywords/cascading-classifiers","display_name":"Cascading classifiers","score":0.5633657574653625},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.49135151505470276},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4441411793231964},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42344942688941956},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39541205763816833},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32323700189590454}],"concepts":[{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.7901877164840698},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.7110961675643921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7051130533218384},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6922483444213867},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6889219284057617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6324832439422607},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5645354986190796},{"id":"https://openalex.org/C40651066","wikidata":"https://www.wikidata.org/wiki/Q5048220","display_name":"Cascading classifiers","level":4,"score":0.5633657574653625},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.49135151505470276},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4441411793231964},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42344942688941956},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39541205763816833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32323700189590454},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon48115.2021.9589181","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon48115.2021.9589181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2021 \u2013 47th Annual Conference of the IEEE Industrial Electronics Society","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":9,"referenced_works":["https://openalex.org/W1966455875","https://openalex.org/W2016375676","https://openalex.org/W2081361638","https://openalex.org/W2106525823","https://openalex.org/W2145940431","https://openalex.org/W2167917621","https://openalex.org/W2755371172","https://openalex.org/W2794124609","https://openalex.org/W2998768810"],"related_works":["https://openalex.org/W2046975922","https://openalex.org/W1933712933","https://openalex.org/W2057416691","https://openalex.org/W4256395896","https://openalex.org/W2968660735","https://openalex.org/W2245276335","https://openalex.org/W1760344465","https://openalex.org/W1985828740","https://openalex.org/W4205397888","https://openalex.org/W3094789558"],"abstract_inverted_index":{"Ensemble":[0],"is":[1,54,68,145],"an":[2],"effective":[3],"method":[4],"to":[5,58,111,118],"integrate":[6],"diverse":[7],"classifiers":[8,25,86,166],"and":[9,31,135,157,176],"take":[10],"advantage":[11],"of":[12,29,39,44,52,62,84,95,154,165],"their":[13],"individual":[14],"strengths.":[15],"Given":[16],"a":[17,27,106],"test":[18,78],"sample,":[19],"ensemble":[20,53,97,185],"selects":[21],"the":[22,33,37,40,45,50,55,60,69,73,77,82,88,93,96,121,146,163,171,181],"most":[23],"competent":[24],"from":[26],"pool":[28,89,164],"candidates":[30],"predicts":[32],"result":[34],"by":[35],"combining":[36],"outputs":[38],"selected":[41],"classifiers.":[42,115],"One":[43],"key":[46],"factors":[47],"that":[48,129,142,167,187],"affects":[49],"performance":[51],"criterion":[56,67],"defined":[57],"evaluate":[59],"competence":[61,83],"classifier.":[63],"The":[64,138],"commonly":[65],"used":[66],"prediction":[70,94],"accuracy":[71],"in":[72,87,152],"local":[74],"region":[75],"around":[76],"sample.":[79],"However,":[80],"if":[81],"all":[85],"are":[90,130,168,180],"not":[91],"qualified,":[92],"will":[98],"be":[99],"inaccurate.":[100],"In":[101,116],"this":[102],"paper,":[103],"we":[104,124],"propose":[105],"hyper-parameter":[107],"optimization":[108,127,151],"based":[109,131],"strategy":[110],"generate":[112],"high":[113,190],"quality":[114],"order":[117],"automatically":[119],"identify":[120],"optimal":[122],"hyper-parameters,":[123,173],"explored":[125],"six":[126],"algorithms":[128],"on":[132],"Gaussian":[133],"Process":[134],"Evolutionary":[136],"theory.":[137],"experiment":[139],"results":[140],"show":[141],"Bayesian":[143],"Optimization":[144],"best":[147],"algorithm":[148],"for":[149],"hyper-parameters":[150],"terms":[153],"time":[155],"consumption":[156],"classification":[158,191],"accuracy.":[159,192],"Furthermore,":[160],"cooperating":[161],"with":[162,170],"configured":[169],"optimized":[172],"Kullback-Leibler":[174],"Divergence":[175],"Randomized":[177],"Reference":[178],"Classifier":[179],"two":[182],"outstanding":[183],"dynamic":[184],"methods":[186],"can":[188],"provide":[189]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}