{"id":"https://openalex.org/W2729287684","doi":"https://doi.org/10.1109/icis.2017.7960103","title":"Multiple kernel learning based feature selection for process monitoring","display_name":"Multiple kernel learning based feature selection for process monitoring","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2729287684","doi":"https://doi.org/10.1109/icis.2017.7960103","mag":"2729287684"},"language":"en","primary_location":{"id":"doi:10.1109/icis.2017.7960103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis.2017.7960103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","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/A5082641677","display_name":"Shenglan Yang","orcid":"https://orcid.org/0000-0001-8307-3712"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sheng-Lan Yang","raw_affiliation_strings":["State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058651829","display_name":"Wei Weng","orcid":"https://orcid.org/0000-0002-8484-9065"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Wei Weng","raw_affiliation_strings":["Graduate School of Information, Waseda University, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Waseda University, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042423456","display_name":"Gang Rong","orcid":"https://orcid.org/0000-0002-6779-2968"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Rong","raw_affiliation_strings":["State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102892083","display_name":"Yiping Feng","orcid":"https://orcid.org/0000-0002-5023-3668"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi-Ping Feng","raw_affiliation_strings":["State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5082641677"],"corresponding_institution_ids":["https://openalex.org/I4391767838"],"apc_list":null,"apc_paid":null,"fwci":0.6035,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.68642735,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"809","last_page":"814"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9995999932289124,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9889000058174133,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9883000254631042,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7536790370941162},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7460903525352478},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.702734112739563},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6362862586975098},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6227315664291382},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6171289682388306},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5727101564407349},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5479519367218018},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4992640018463135},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.4792639911174774},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42043182253837585},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40789806842803955},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3183146119117737},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19960981607437134},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08674874901771545}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7536790370941162},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7460903525352478},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.702734112739563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6362862586975098},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6227315664291382},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6171289682388306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5727101564407349},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5479519367218018},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4992640018463135},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.4792639911174774},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42043182253837585},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40789806842803955},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3183146119117737},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19960981607437134},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08674874901771545},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icis.2017.7960103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis.2017.7960103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","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":0.550000011920929}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1808644423","https://openalex.org/W1981552604","https://openalex.org/W2004186751","https://openalex.org/W2063978378","https://openalex.org/W2108995755","https://openalex.org/W2109743529","https://openalex.org/W2125055259","https://openalex.org/W2131377090","https://openalex.org/W2143426320","https://openalex.org/W2154053567","https://openalex.org/W2154462399","https://openalex.org/W2155694896","https://openalex.org/W2156909104","https://openalex.org/W2158001550","https://openalex.org/W2160540263","https://openalex.org/W2275031392","https://openalex.org/W2610681481","https://openalex.org/W2998216295","https://openalex.org/W3104887532","https://openalex.org/W4239510810","https://openalex.org/W4249625715","https://openalex.org/W4285719527","https://openalex.org/W6638249342","https://openalex.org/W6683181193","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W2900715739","https://openalex.org/W2289496068","https://openalex.org/W2043864454","https://openalex.org/W2547116720","https://openalex.org/W2188831877","https://openalex.org/W3125885229","https://openalex.org/W4291669689","https://openalex.org/W2153379791","https://openalex.org/W2001173190","https://openalex.org/W2295320501"],"abstract_inverted_index":{"In":[0],"the":[1,24,33,39,42,64,70,81,96,101,103,114,120,123,132,135,147],"process":[2,10,43,65,142],"of":[3,28,41,72,76,84,134],"large-scale":[4],"chemical":[5],"engineering,":[6],"more":[7],"useful":[8],"industrial":[9],"information":[11],"can":[12],"be":[13,61],"obtained":[14],"by":[15],"increasing":[16],"measuring":[17],"variables,":[18],"defined":[19],"as":[20],"system":[21],"features.":[22,85],"However,":[23],"increase":[25],"in":[26,113,126,141,149],"amount":[27],"features":[29,51,79],"will":[30],"lead":[31],"to":[32],"high":[34],"computation":[35],"cost":[36],"and":[37],"reduce":[38],"efficiency":[40],"monitoring":[44,143],"system.":[45],"To":[46],"solve":[47],"this":[48],"issue,":[49],"those":[50],"that":[52],"are":[53],"redundant":[54],"or":[55],"bring":[56],"an":[57,110],"incorrect":[58],"result":[59],"should":[60],"removed":[62],"before":[63],"monitoring.":[66],"Feature":[67],"selection":[68,105,129,140],"is":[69,93,107,144],"issue":[71],"selecting":[73],"a":[74],"subset":[75],"most":[77],"informative":[78],"from":[80],"full":[82],"set":[83],"A":[86],"novel":[87],"multiple":[88,115],"kernel":[89,116],"learning":[90],"based":[91],"method":[92,137],"proposed":[94,136],"for":[95,138],"feature":[97,104,128,139],"selection.":[98],"Distinguished":[99],"with":[100],"literature,":[102],"problem":[106,112,125],"transformed":[108],"into":[109],"optimization":[111],"learning,":[117],"which":[118],"avoids":[119],"drawback":[121],"called":[122],"\u201cmonotonic\u201d":[124],"conventional":[127],"methods.":[130],"Finally,":[131],"performance":[133],"demonstrated":[145],"through":[146],"experiment":[148],"Tennessee":[150],"Eastman":[151],"benchmark":[152],"process.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}