{"id":"https://openalex.org/W3090896493","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207215","title":"Instance-Based Ensemble Selection Using Deep Reinforcement Learning","display_name":"Instance-Based Ensemble Selection Using Deep Reinforcement Learning","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090896493","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207215","mag":"3090896493"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5017099220","display_name":"Zhengshang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Zhengshang Liu","raw_affiliation_strings":["School of Computing and Information Systems, The University of Melbourne, Parkville, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, The University of Melbourne, Parkville, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069895484","display_name":"Kotagiri Ramamohanarao","orcid":"https://orcid.org/0000-0003-3304-9268"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kotagiri Ramamohanarao","raw_affiliation_strings":["School of Computing and Information Systems, The University of Melbourne, Parkville, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, The University of Melbourne, Parkville, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017099220"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.12211508,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9997000098228455,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9997000098228455,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9977999925613403,"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":0.9969000220298767,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8976796865463257},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.808476448059082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7035719156265259},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6231788992881775},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6093870401382446},{"id":"https://openalex.org/keywords/learning-classifier-system","display_name":"Learning classifier system","score":0.5595875978469849},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.525590181350708},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.49135246872901917}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8976796865463257},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.808476448059082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7035719156265259},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6231788992881775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6093870401382446},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.5595875978469849},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.525590181350708},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.49135246872901917}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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":34,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W32403112","https://openalex.org/W41554520","https://openalex.org/W1492936035","https://openalex.org/W1496929357","https://openalex.org/W1562197959","https://openalex.org/W1565746575","https://openalex.org/W1587506815","https://openalex.org/W1931284523","https://openalex.org/W1980264541","https://openalex.org/W1988790447","https://openalex.org/W1990079212","https://openalex.org/W2016159616","https://openalex.org/W2016409481","https://openalex.org/W2023294425","https://openalex.org/W2074466695","https://openalex.org/W2095705004","https://openalex.org/W2117897510","https://openalex.org/W2121213623","https://openalex.org/W2121863487","https://openalex.org/W2135293965","https://openalex.org/W2145339207","https://openalex.org/W2161390635","https://openalex.org/W2170375089","https://openalex.org/W2585663194","https://openalex.org/W2911964244","https://openalex.org/W3120740533","https://openalex.org/W4244777963","https://openalex.org/W4285719527","https://openalex.org/W6629236817","https://openalex.org/W6629795882","https://openalex.org/W6633883679","https://openalex.org/W6674330103","https://openalex.org/W6684003512"],"related_works":["https://openalex.org/W1966456942","https://openalex.org/W2754028433","https://openalex.org/W3148138296","https://openalex.org/W1882507001","https://openalex.org/W2367922714","https://openalex.org/W1986508893","https://openalex.org/W2352650970","https://openalex.org/W4383501708","https://openalex.org/W2130711276","https://openalex.org/W3135573334"],"abstract_inverted_index":{"Ensemble":[0],"selection":[1,107],"is":[2,29,136],"a":[3,16,21,31],"very":[4],"active":[5],"research":[6],"topic":[7],"in":[8,34,121],"machine":[9],"learning":[10,46],"area.":[11],"It":[12],"aims":[13],"to":[14,61,98,127,138],"achieve":[15],"better":[17],"performance":[18,71],"by":[19],"selecting":[20],"proper":[22],"subset":[23],"of":[24,72,81,131],"the":[25,63,69,75,79,128],"original":[26],"ensemble,":[27],"which":[28],"essentially":[30],"searching":[32],"problem":[33],"large":[35,122],"combinatorial":[36],"spaces.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41,57],"propose":[42],"an":[43],"instance-based":[44],"reinforcement":[45,132],"(IBRL)":[47],"model,":[48],"that":[49,87],"selects":[50],"distinct":[51],"subsets":[52,91],"for":[53,92,105],"different":[54,93],"instances.":[55,94],"Specifically,":[56],"use":[58],"deep":[59],"Q-network":[60],"approximate":[62],"optimal":[64],"policy.":[65],"Rather":[66],"than":[67],"considering":[68],"overall":[70],"each":[73],"classifier,":[74],"network":[76],"learns":[77],"from":[78],"feedback":[80],"classifiers":[82],"on":[83],"individual":[84],"instance,":[85],"so":[86],"it":[88,117],"generates":[89,113],"non-static":[90],"Experiments":[95],"are":[96],"conducted":[97],"compare":[99],"our":[100,134],"model":[101,135],"against":[102],"state-of-the-art":[103],"approaches":[104],"both":[106],"and":[108,116],"combination.":[109],"The":[110],"proposed":[111],"method":[112],"promising":[114],"results":[115],"shows":[118],"exceptional":[119],"advantage":[120],"scale":[123],"distributed":[124],"environment.":[125],"Due":[126],"environment-free":[129],"characteristic":[130],"learning,":[133],"adaptable":[137],"various":[139],"real":[140],"world":[141],"tasks":[142],"with":[143],"minimal":[144],"changes.":[145]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}