{"id":"https://openalex.org/W2809988976","doi":"https://doi.org/10.1109/fskd.2017.8393311","title":"A novel technique to prune variable selection ensembles","display_name":"A novel technique to prune variable selection ensembles","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2809988976","doi":"https://doi.org/10.1109/fskd.2017.8393311","mag":"2809988976"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5021233697","display_name":"Liangpin Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liang-Pin Ren","raw_affiliation_strings":["School of Software and Applied Technology, Zhengzhou University, Zhengzhou, Henan, China"],"affiliations":[{"raw_affiliation_string":"School of Software and Applied Technology, Zhengzhou University, Zhengzhou, Henan, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100749933","display_name":"Chunxia Zhang","orcid":"https://orcid.org/0000-0001-9639-4507"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chun-Xia Zhang","raw_affiliation_strings":["School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078580053","display_name":"Haiyan Xuan","orcid":"https://orcid.org/0000-0002-3476-7926"},"institutions":[{"id":"https://openalex.org/I22716506","display_name":"Lanzhou University of Technology","ror":"https://ror.org/03panb555","country_code":"CN","type":"education","lineage":["https://openalex.org/I22716506"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai-Yan Xuan","raw_affiliation_strings":["School of Economics and Management Lanzhou University of Technology, Lanzhou, Gansu, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management Lanzhou University of Technology, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I22716506"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021233697"],"corresponding_institution_ids":["https://openalex.org/I38877650"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.24328038,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"449","last_page":"454"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9980000257492065,"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":0.9926999807357788,"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/computer-science","display_name":"Computer science","score":0.732086718082428},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6637845039367676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6104741096496582},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.6033358573913574},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5719162821769714},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5478874444961548},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5467929840087891},{"id":"https://openalex.org/keywords/sort","display_name":"sort","score":0.5379913449287415},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5146787762641907},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5054044723510742},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.49034714698791504},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.48811373114585876},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.47028660774230957},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3611820936203003},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16097936034202576}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.732086718082428},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6637845039367676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6104741096496582},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.6033358573913574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5719162821769714},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5478874444961548},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5467929840087891},{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.5379913449287415},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5146787762641907},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5054044723510742},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.49034714698791504},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.48811373114585876},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.47028660774230957},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3611820936203003},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16097936034202576},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1995276998","https://openalex.org/W2004014581","https://openalex.org/W2010741163","https://openalex.org/W2017008479","https://openalex.org/W2037226761","https://openalex.org/W2041459958","https://openalex.org/W2061082730","https://openalex.org/W2065165463","https://openalex.org/W2071949631","https://openalex.org/W2074682976","https://openalex.org/W2103346566","https://openalex.org/W2108273125","https://openalex.org/W2135046866","https://openalex.org/W2149584226","https://openalex.org/W2168951536","https://openalex.org/W2169103656","https://openalex.org/W2385355890","https://openalex.org/W2518421866","https://openalex.org/W2562162676","https://openalex.org/W2604377165","https://openalex.org/W2911964244","https://openalex.org/W3037000643","https://openalex.org/W3099723433","https://openalex.org/W3121452939","https://openalex.org/W4212883601","https://openalex.org/W4247649469","https://openalex.org/W4300819821","https://openalex.org/W6684653394"],"related_works":["https://openalex.org/W2361805396","https://openalex.org/W2972254340","https://openalex.org/W2022231341","https://openalex.org/W1805912688","https://openalex.org/W4255476312","https://openalex.org/W2373973507","https://openalex.org/W2351154965","https://openalex.org/W2357579988","https://openalex.org/W2373300491","https://openalex.org/W2018632273"],"abstract_inverted_index":{"In":[0,31,93],"ensemble":[1,10,19,29,171],"learning":[2,11],"field,":[3],"it":[4,36,164],"has":[5],"been":[6],"proven":[7],"that":[8,40,163],"selective":[9],"(i.e.,":[12],"only":[13,140],"fusing":[14],"some":[15,142],"instead":[16],"of":[17,27,128,150],"all":[18,82],"members)":[20],"can":[21],"further":[22],"improve":[23],"the":[24,52,59,66,70,83,117,124,131,148,151,168],"prediction":[25],"ability":[26],"an":[28,87,100],"machine.":[30],"this":[32,50,94],"paper,":[33,95],"we":[34,96,115],"apply":[35],"in":[37,45],"another":[38],"framework,":[39],"is,":[41],"variable":[42,76],"selection":[43,77],"problems":[44],"linear":[46],"regression":[47],"models.":[48],"Under":[49],"situation,":[51],"main":[53],"goal":[54],"is":[55,137],"to":[56,73,85,98,123],"accurately":[57],"detect":[58],"variables":[60],"which":[61],"have":[62],"real":[63],"influence":[64],"on":[65,154],"response.":[67],"As":[68],"for":[69,90],"existing":[71],"algorithms":[72],"construct":[74],"a":[75,105,112,135],"ensemble,":[78],"they":[79],"generally":[80],"combine":[81],"members":[84,118,143],"create":[86],"importance":[88],"measure":[89],"each":[91,127],"variable.":[92],"propose":[97],"insert":[99],"additional":[101],"pruning":[102],"phase":[103],"into":[104],"state-of-the-art":[106],"algorithm":[107],"ST2E":[108,121],"[14].":[109],"By":[110],"defining":[111],"reference":[113,132],"vector,":[114],"sort":[116],"generated":[119],"by":[120,139],"according":[122],"angle":[125],"between":[126],"them":[129],"and":[130],"vector.":[133],"Then,":[134],"subensemble":[136],"obtained":[138],"keeping":[141],"ranked":[144],"ahead.":[145],"We":[146],"investigated":[147],"performance":[149],"proposed":[152],"method":[153],"several":[155,175],"simulated":[156],"data":[157],"sets.":[158],"The":[159],"experimental":[160],"results":[161],"show":[162],"performs":[165],"better":[166],"than":[167],"original":[169],"full":[170],"as":[172,174],"well":[173],"other":[176],"rivals.":[177]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
