{"id":"https://openalex.org/W2772527704","doi":"https://doi.org/10.1109/iecon.2017.8217016","title":"Leaf node-level ensemble pruning approaches based on node-sample correlation for random forest","display_name":"Leaf node-level ensemble pruning approaches based on node-sample correlation for random forest","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2772527704","doi":"https://doi.org/10.1109/iecon.2017.8217016","mag":"2772527704"},"language":"en","primary_location":{"id":"doi:10.1109/iecon.2017.8217016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2017.8217016","pdf_url":null,"source":{"id":"https://openalex.org/S4363608531","display_name":"IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"IECON 2017 - 43rd 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/A5100352274","display_name":"Xin Liu","orcid":"https://orcid.org/0000-0002-8272-9553"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Liu","raw_affiliation_strings":["Department of Automation, Xiamen Univerist","Shenzhen Research Institute of Xiamen Univeristy, Xiamen, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen Univerist","institution_ids":["https://openalex.org/I75867142"]},{"raw_affiliation_string":"Shenzhen Research Institute of Xiamen Univeristy, Xiamen, P. R. China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034395042","display_name":"Qifeng Zhou","orcid":"https://orcid.org/0000-0003-3583-6943"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qifeng Zhou","raw_affiliation_strings":["Department of Automation, Xiamen Univerist","Shenzhen Research Institute of Xiamen Univeristy, Xiamen, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen Univerist","institution_ids":["https://openalex.org/I75867142"]},{"raw_affiliation_string":"Shenzhen Research Institute of Xiamen Univeristy, Xiamen, P. R. China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045825296","display_name":"Fan Yang","orcid":"https://orcid.org/0000-0002-3523-1138"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Yang","raw_affiliation_strings":["Department of Automation, Xiamen Univerist","Shenzhen Research Institute of Xiamen Univeristy, Xiamen, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, Xiamen Univerist","institution_ids":["https://openalex.org/I75867142"]},{"raw_affiliation_string":"Shenzhen Research Institute of Xiamen Univeristy, Xiamen, P. R. China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I75867142"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15445657,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"5859","last_page":"5864"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9783999919891357,"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/T10320","display_name":"Neural Networks and Applications","score":0.9783999919891357,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9690999984741211,"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/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9559000134468079,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7253116369247437},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6979514360427856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6696269512176514},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6688299775123596},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6330219507217407},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5227189660072327},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5104164481163025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5074228644371033},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4832080602645874},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44628143310546875},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.43439170718193054},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4315488636493683},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4252413511276245},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41533344984054565},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.41525983810424805},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40645813941955566},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30481937527656555}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7253116369247437},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6979514360427856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6696269512176514},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6688299775123596},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6330219507217407},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5227189660072327},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5104164481163025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5074228644371033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4832080602645874},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44628143310546875},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.43439170718193054},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4315488636493683},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4252413511276245},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41533344984054565},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.41525983810424805},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40645813941955566},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30481937527656555},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon.2017.8217016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2017.8217016","pdf_url":null,"source":{"id":"https://openalex.org/S4363608531","display_name":"IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W273955616","https://openalex.org/W619572877","https://openalex.org/W1071888377","https://openalex.org/W1520812622","https://openalex.org/W1560107318","https://openalex.org/W1731081199","https://openalex.org/W1797580880","https://openalex.org/W1979495315","https://openalex.org/W1980264541","https://openalex.org/W2008667922","https://openalex.org/W2011114139","https://openalex.org/W2016758789","https://openalex.org/W2036547589","https://openalex.org/W2100805904","https://openalex.org/W2103346566","https://openalex.org/W2109518982","https://openalex.org/W2111691418","https://openalex.org/W2127082287","https://openalex.org/W2132914434","https://openalex.org/W2152761983","https://openalex.org/W2515179890","https://openalex.org/W2557732161","https://openalex.org/W2592790288","https://openalex.org/W3037729706","https://openalex.org/W3120740533","https://openalex.org/W4210949798","https://openalex.org/W4249972823","https://openalex.org/W6619402555","https://openalex.org/W6633497745","https://openalex.org/W6679322618","https://openalex.org/W6729764905","https://openalex.org/W6734349242"],"related_works":["https://openalex.org/W4361806667","https://openalex.org/W4293151273","https://openalex.org/W1566212037","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W2944292463","https://openalex.org/W4388550696","https://openalex.org/W3014252901","https://openalex.org/W4321636153","https://openalex.org/W4313289487"],"abstract_inverted_index":{"As":[0],"a":[1,9,23,64,71],"state-of-the-art":[2],"ensemble":[3,30,110,135],"method,":[4],"random":[5,55,65],"forest":[6,56,66],"which":[7],"exhibits":[8],"good":[10],"ability":[11],"to":[12,95,101],"predict":[13],"and":[14,31,77,88,97,130],"generalize":[15],"on":[16],"various":[17],"dataset":[18],"is":[19,68,78,94],"often":[20],"composed":[21],"of":[22,26,29,127,142],"large":[24],"number":[25],"trees.":[27],"Redundancy":[28],"connotative":[32],"decision":[33],"rules":[34,99,113,133],"result":[35],"in":[36,44],"expensive":[37],"operational":[38],"costs":[39],"as":[40,42,70],"well":[41],"difficulties":[43],"comprehension.":[45],"In":[46],"this":[47],"paper,":[48],"novel":[49],"leaf":[50,60],"node-level":[51],"pruning":[52,87,90],"methods":[53,121],"for":[54,81],"are":[57],"proposed.":[58],"Each":[59],"node":[61],"extracted":[62],"from":[63,84,108],"model":[67],"regarded":[69],"singe":[72],"classifier":[73],"or":[74,111],"classification":[75],"rule,":[76],"then":[79],"evaluated":[80],"pruning.":[82],"Different":[83],"traditional":[85],"tree-level":[86],"rule":[89,128],"methods,":[91],"the":[92,109,119,125,131],"idea":[93],"evaluate":[96],"extract":[98],"according":[100],"node-sample":[102],"correlation":[103],"rather":[104],"than":[105],"eliminate":[106],"trees":[107],"integrate":[112],"themselves.":[114],"Experiment":[115],"results":[116],"show":[117],"that":[118],"proposed":[120],"can":[122],"efficiently":[123],"reduce":[124],"size":[126],"set,":[129],"resulted":[132],"based":[134],"achieves":[136],"better":[137],"interpretability":[138],"without":[139],"significant":[140],"loss":[141],"accuracy.":[143]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}