{"id":"https://openalex.org/W4205287640","doi":"https://doi.org/10.3390/sym14010149","title":"Online Streaming Features Selection via Markov Blanket","display_name":"Online Streaming Features Selection via Markov Blanket","publication_year":2022,"publication_date":"2022-01-13","ids":{"openalex":"https://openalex.org/W4205287640","doi":"https://doi.org/10.3390/sym14010149"},"language":"en","primary_location":{"id":"doi:10.3390/sym14010149","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym14010149","pdf_url":"https://www.mdpi.com/2073-8994/14/1/149/pdf?version=1642062136","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/14/1/149/pdf?version=1642062136","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001866583","display_name":"Waqar Ali Khan","orcid":"https://orcid.org/0000-0002-9772-1167"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Waqar Khan","raw_affiliation_strings":["School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022434722","display_name":"Lingfu Kong","orcid":"https://orcid.org/0000-0001-7696-1412"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lingfu Kong","raw_affiliation_strings":["School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083091566","display_name":"Brekhna Brekhna","orcid":null},"institutions":[{"id":"https://openalex.org/I1308893265","display_name":"Shaheed Benazir Bhutto University","ror":"https://ror.org/02zwhz281","country_code":"PK","type":"education","lineage":["https://openalex.org/I1308893265"]},{"id":"https://openalex.org/I59483232","display_name":"Shandong University of Finance and Economics","ror":"https://ror.org/02e2nnq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I59483232"]}],"countries":["CN","PK"],"is_corresponding":false,"raw_author_name":"Brekhna Brekhna","raw_affiliation_strings":["Department of Computer Science, Shaheed Benazir Bhutto University, Peshawar 25000, Pakistan","School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Shaheed Benazir Bhutto University, Peshawar 25000, Pakistan","institution_ids":["https://openalex.org/I1308893265"]},{"raw_affiliation_string":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China","institution_ids":["https://openalex.org/I59483232"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100398729","display_name":"Ling Wang","orcid":"https://orcid.org/0000-0003-4098-7906"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Wang","raw_affiliation_strings":["School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025568713","display_name":"Huigui Yan","orcid":"https://orcid.org/0009-0001-4271-7590"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huigui Yan","raw_affiliation_strings":["School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China","institution_ids":["https://openalex.org/I39333907"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5022434722"],"corresponding_institution_ids":["https://openalex.org/I39333907"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.2653,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58316748,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"14","issue":"1","first_page":"149","last_page":"149"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","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"}},"topics":[{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","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":0.9955000281333923,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9950000047683716,"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/markov-blanket","display_name":"Markov blanket","score":0.9785683155059814},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7775899171829224},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7587395310401917},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6737947463989258},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5815651416778564},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5502699017524719},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5278273224830627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5112979412078857},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4752994477748871},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.47197991609573364},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4523235261440277},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4450143575668335},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.2325948178768158},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17673194408416748},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1391162872314453},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.12345889210700989}],"concepts":[{"id":"https://openalex.org/C123867240","wikidata":"https://www.wikidata.org/wiki/Q3001792","display_name":"Markov blanket","level":5,"score":0.9785683155059814},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7775899171829224},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7587395310401917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6737947463989258},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5815651416778564},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5502699017524719},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5278273224830627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5112979412078857},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4752994477748871},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.47197991609573364},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4523235261440277},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4450143575668335},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.2325948178768158},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17673194408416748},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1391162872314453},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.12345889210700989},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym14010149","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym14010149","pdf_url":"https://www.mdpi.com/2073-8994/14/1/149/pdf?version=1642062136","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e58208b4cd8440778d8b411fae484b3c","is_oa":true,"landing_page_url":"https://doaj.org/article/e58208b4cd8440778d8b411fae484b3c","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 14, Iss 1, p 149 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/14/1/149/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/sym14010149","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym14010149","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym14010149","pdf_url":"https://www.mdpi.com/2073-8994/14/1/149/pdf?version=1642062136","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4205287640.pdf","grobid_xml":"https://content.openalex.org/works/W4205287640.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W149574399","https://openalex.org/W1524326598","https://openalex.org/W2109306884","https://openalex.org/W2121273346","https://openalex.org/W2153338628","https://openalex.org/W2257550357","https://openalex.org/W2324228145","https://openalex.org/W2587807052","https://openalex.org/W2785054398","https://openalex.org/W2796654576","https://openalex.org/W2905193210","https://openalex.org/W2910636188","https://openalex.org/W2912882859","https://openalex.org/W2964037464","https://openalex.org/W2973192395","https://openalex.org/W2980507899","https://openalex.org/W2996966849","https://openalex.org/W3014714271","https://openalex.org/W3016344614","https://openalex.org/W3022655582","https://openalex.org/W3025628315","https://openalex.org/W3030884648","https://openalex.org/W3035574168","https://openalex.org/W3037322243","https://openalex.org/W3047753861","https://openalex.org/W3082866995","https://openalex.org/W3086035625","https://openalex.org/W3091533194","https://openalex.org/W3160262499","https://openalex.org/W3161175575","https://openalex.org/W3193978175","https://openalex.org/W3200378786","https://openalex.org/W6682572403","https://openalex.org/W6692257784","https://openalex.org/W6772083016"],"related_works":["https://openalex.org/W2123587139","https://openalex.org/W2797721853","https://openalex.org/W2510548579","https://openalex.org/W49111948","https://openalex.org/W4206306045","https://openalex.org/W2307705085","https://openalex.org/W3186106104","https://openalex.org/W3023326395","https://openalex.org/W23237351","https://openalex.org/W2159351263"],"abstract_inverted_index":{"Streaming":[0,156],"feature":[1,30,116,157],"selection":[2,31,158],"has":[3],"always":[4],"been":[5],"an":[6],"excellent":[7],"method":[8],"for":[9],"selecting":[10,310],"the":[11,33,53,95,103,111,114,122,177,247,250,293,300,311],"relevant":[12],"subset":[13],"of":[14,55,97,191,208,233,246,249],"features":[15,124,251],"from":[16,125],"high-dimensional":[17],"data":[18],"and":[19,48,61,89,101,106,117,128,132,135,153,155,163,172,182,193,198,216,229,237,269,279,286],"overcoming":[20],"learning":[21,43,146],"complexity.":[22],"However,":[23],"little":[24],"attention":[25],"is":[26,59,141,306],"paid":[27],"to":[28,109],"online":[29,104],"through":[32],"Markov":[34,76],"Blanket":[35,77],"(MB).":[36],"Several":[37],"studies":[38],"based":[39,79,259],"on":[40,80,165,195,260,273],"traditional":[41,144],"MB":[42,145,244],"presented":[44],"low":[45],"prediction":[46,262],"accuracy":[47,88,223,263],"used":[49,186],"fewer":[50],"datasets":[51,171,213,275],"as":[52],"number":[54,96],"conditional":[56,83,98],"independence":[57,84,99],"tests":[58,100],"high":[60,87],"consumes":[62],"more":[63,242,307],"time.":[64,92],"This":[65],"paper":[66],"presents":[67],"a":[68,81,188,205],"novel":[69],"algorithm":[70],"called":[71],"Online":[72],"Feature":[73],"Selection":[74],"Via":[75],"(OFSVMB)":[78],"statistical":[82],"test":[85],"offering":[86],"less":[90],"computation":[91],"It":[93,240],"reduces":[94],"incorporates":[102],"relevance":[105],"redundant":[107,123],"analysis":[108,302],"check":[110],"relevancy":[112],"between":[113],"upcoming":[115],"target":[118],"variable":[119],"T,":[120],"discard":[121],"Parents-Child":[126],"(PC)":[127],"Spouses":[129],"(SP)":[130],"online,":[131],"find":[133,292],"PC":[134,294],"SP":[136],"simultaneously.":[137],"The":[138],"performance":[139,178],"OFSVMB":[140,220,255,305],"compared":[142],"with":[143,187,214,267],"algorithms":[147,159,290],"including":[148,160,201],"IAMB,":[149,225],"STMB,":[150,226],"HITON-MB,":[151,227],"BAMB,":[152,228],"EEMB,":[154],"OSFS,":[161,277,284],"Alpha-investing,":[162,278,285],"SAOLA":[164,280,287],"9":[166],"benchmark":[167,196,211],"Bayesian":[168],"Network":[169],"(BN)":[170],"14":[173],"real-world":[174,199,274],"datasets.":[175],"For":[176],"evaluation,":[179],"F1,":[180,234],"precision,":[181,235],"recall":[183],"measures":[184],"are":[185],"significant":[189,206,222],"level":[190,207],"0.01":[192],"0.05":[194],"BN":[197,212],"datasets,":[200],"12":[202,265],"classifiers":[203,266],"keeping":[204],"0.01.":[209],"On":[210],"500":[215],"5000":[217],"sample":[218,271],"sizes,":[219],"achieved":[221],"than":[224,276,283],"EEMB":[230],"in":[231,309],"terms":[232],"recall,":[236],"running":[238],"faster.":[239],"finds":[241],"accurate":[243,308],"regardless":[245],"size":[248],"set.":[252],"In":[253],"contrast,":[254],"offers":[256],"substantial":[257],"improvements":[258],"mean":[261],"regarding":[264],"small":[268],"large":[270],"sizes":[272],"but":[281,296],"slower":[282],"because":[288],"these":[289],"only":[291],"set":[295],"not":[297],"SP.":[298],"Furthermore,":[299],"sensitivity":[301],"shows":[303],"that":[304],"optimal":[312],"features.":[313]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
