{"id":"https://openalex.org/W2568800759","doi":"https://doi.org/10.1109/iecon.2016.7793952","title":"Key data set selection algorithm based on PLS regression in industrial process","display_name":"Key data set selection algorithm based on PLS regression in industrial process","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2568800759","doi":"https://doi.org/10.1109/iecon.2016.7793952","mag":"2568800759"},"language":"en","primary_location":{"id":"doi:10.1109/iecon.2016.7793952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2016.7793952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2016 - 42nd 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/A5101785543","display_name":"Mingyang Yang","orcid":"https://orcid.org/0009-0006-7112-4838"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyang Yang","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004201510","display_name":"Xuebo Yang","orcid":"https://orcid.org/0000-0002-9867-0488"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuebo Yang","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083390109","display_name":"Chengming Yang","orcid":"https://orcid.org/0000-0003-3498-7352"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengming Yang","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003410899","display_name":"Hongpeng Zhou","orcid":"https://orcid.org/0000-0002-3894-0116"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongpeng Zhou","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27300931,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"95","issue":null,"first_page":"7179","last_page":"7184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7165111899375916},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.6796003580093384},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.656562328338623},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6226804852485657},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6105464100837708},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5700211524963379},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5541269183158875},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5510334968566895},{"id":"https://openalex.org/keywords/ordinary-least-squares","display_name":"Ordinary least squares","score":0.5322012305259705},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4700024724006653},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46002426743507385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40674781799316406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37180376052856445},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1508922576904297},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1434268057346344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7165111899375916},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.6796003580093384},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.656562328338623},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6226804852485657},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6105464100837708},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5700211524963379},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5541269183158875},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5510334968566895},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.5322012305259705},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4700024724006653},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46002426743507385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40674781799316406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37180376052856445},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1508922576904297},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1434268057346344},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon.2016.7793952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2016.7793952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1965289315","https://openalex.org/W1966538260","https://openalex.org/W1966793708","https://openalex.org/W1973164954","https://openalex.org/W1984672166","https://openalex.org/W1996195892","https://openalex.org/W2002448960","https://openalex.org/W2017014583","https://openalex.org/W2018338598","https://openalex.org/W2019175010","https://openalex.org/W2022863828","https://openalex.org/W2037226761","https://openalex.org/W2052828853","https://openalex.org/W2060188270","https://openalex.org/W2060659046","https://openalex.org/W2061593315","https://openalex.org/W2081829568","https://openalex.org/W2084169316","https://openalex.org/W2093244782","https://openalex.org/W2094396763","https://openalex.org/W2098722265","https://openalex.org/W2102667102","https://openalex.org/W2138019504","https://openalex.org/W2156571267","https://openalex.org/W2162384717","https://openalex.org/W2167191085","https://openalex.org/W2170917242","https://openalex.org/W2396501762","https://openalex.org/W2400810967","https://openalex.org/W2473060649","https://openalex.org/W3014064419","https://openalex.org/W6682904970","https://openalex.org/W6684759661","https://openalex.org/W6712490571","https://openalex.org/W6720609026"],"related_works":["https://openalex.org/W1989457222","https://openalex.org/W2429686810","https://openalex.org/W2371059786","https://openalex.org/W2069932138","https://openalex.org/W2023475031","https://openalex.org/W2075634219","https://openalex.org/W2527896606","https://openalex.org/W583826391","https://openalex.org/W1999916546","https://openalex.org/W1897191184"],"abstract_inverted_index":{"In":[0],"this":[1,32],"paper,":[2],"based":[3],"on":[4,100,114],"the":[5,16,22,43,56,65,72,82,86,91,101,107,125,128,146,150,157],"traditional":[6],"Partial":[7],"Least":[8],"Squares(PLS)":[9],"algorithm,":[10],"a":[11,51,111,136,161],"new":[12,33,137],"way":[13],"to":[14,63],"select":[15],"most":[17,68,87],"effective":[18],"data":[19,57,74,83,140],"sets":[20,58,84],"for":[21],"PLS":[23,103,130],"regression":[24],"is":[25,35,78,143],"proposed.":[26,144],"The":[27,67],"reason":[28],"why":[29],"we":[30],"apply":[31],"approach":[34,152],"that":[36,79],"it":[37,80],"could":[38,118,153],"maintain":[39],"or":[40],"even":[41],"surpass":[42],"original":[44],"performance":[45,113],"of":[46,50,71,90,93,109,139,149,160],"control":[47],"and":[48,105,116,127],"diagnosis":[49],"certain":[52],"process":[53],"while":[54],"keep":[55],"as":[59,61],"less":[60,96],"possible":[62],"enhance":[64],"conciseness.":[66],"significant":[69],"advantage":[70],"proposed":[73,151],"set":[75,141],"selection":[76,142],"method":[77,138],"identifies":[81],"with":[85,106],"typical":[88],"characteristics":[89],"group":[92],"data,":[94],"excluding":[95],"informative":[97],"data.":[98],"Based":[99],"ordinary":[102,126],"algorithm":[104,131],"improvement":[108],"conciseness,":[110],"better":[112],"prediction":[115],"fitting":[117],"be":[119,154],"achieved.":[120],"To":[121],"achieve":[122],"these":[123],"goals,":[124],"improved":[129],"are":[132],"introduced,":[133],"after":[134],"which":[135],"Specifically,":[145],"enhanced":[147],"effectiveness":[148],"revealed":[155],"by":[156],"simulation":[158],"results":[159],"numerical":[162],"case.":[163]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
