{"id":"https://openalex.org/W2789660531","doi":"https://doi.org/10.1109/wpmc.2017.8301854","title":"Feature selection based on network maximal correlation","display_name":"Feature selection based on network maximal correlation","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2789660531","doi":"https://doi.org/10.1109/wpmc.2017.8301854","mag":"2789660531"},"language":"en","primary_location":{"id":"doi:10.1109/wpmc.2017.8301854","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wpmc.2017.8301854","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC)","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/A5019708391","display_name":"Xiaokang Yang","orcid":"https://orcid.org/0000-0003-4029-3322"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaokang Yang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025187681","display_name":"Qiang Wang","orcid":"https://orcid.org/0000-0002-9392-475X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100784213","display_name":"Yi Wang","orcid":"https://orcid.org/0000-0001-8659-4724"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019708391"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.16411854,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"5","issue":null,"first_page":"448","last_page":"452"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9908999800682068,"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":0.9785000085830688,"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/feature-selection","display_name":"Feature selection","score":0.8712947368621826},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7627743482589722},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6878775954246521},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6514109373092651},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5833343267440796},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5230922698974609},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48740914463996887},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4495127499103546},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4417722523212433},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4341599941253662},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42880138754844666},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.41765648126602173},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4119243025779724},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24046096205711365}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.8712947368621826},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7627743482589722},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6878775954246521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6514109373092651},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5833343267440796},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5230922698974609},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48740914463996887},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4495127499103546},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4417722523212433},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4341599941253662},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42880138754844666},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.41765648126602173},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4119243025779724},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24046096205711365},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wpmc.2017.8301854","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wpmc.2017.8301854","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"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":18,"referenced_works":["https://openalex.org/W1530718924","https://openalex.org/W1567784974","https://openalex.org/W2004891386","https://openalex.org/W2018582985","https://openalex.org/W2030748132","https://openalex.org/W2036887984","https://openalex.org/W2099111195","https://openalex.org/W2120526231","https://openalex.org/W2143426320","https://openalex.org/W2151967815","https://openalex.org/W2156571267","https://openalex.org/W2210387432","https://openalex.org/W2556383977","https://openalex.org/W2962679938","https://openalex.org/W3100732768","https://openalex.org/W4248624814","https://openalex.org/W4292402161","https://openalex.org/W6682904970"],"related_works":["https://openalex.org/W4255837520","https://openalex.org/W2387011115","https://openalex.org/W4234808182","https://openalex.org/W2382043075","https://openalex.org/W2809151339","https://openalex.org/W2360673138","https://openalex.org/W2809370583","https://openalex.org/W2333722679","https://openalex.org/W4255628145","https://openalex.org/W2093320919"],"abstract_inverted_index":{"Feature":[0,113],"selection":[1,58,121],"can":[2,94,134],"effectively":[3,97],"increase":[4,46],"the":[5,12,15,45,74,99,111,131],"accuracy":[6],"of":[7,14,62,73],"machine":[8,29],"learning":[9],"and":[10,31,42,64,96,105,149],"improve":[11],"efficiency":[13],"algorithm.":[16],"Therefore,":[17],"it":[18],"has":[19,32],"emerged":[20],"as":[21],"a":[22,51,70,86,117],"critical":[23],"technology":[24],"related":[25],"to":[26,54],"data":[27,48,143],"mining,":[28],"learning,":[30],"shown":[33],"great":[34],"impacts":[35],"in":[36,47,60],"many":[37,55],"applications,":[38],"including":[39],"biomedical,":[40],"financial":[41],"communication.":[43],"However,":[44],"dimension":[49],"poses":[50],"serious":[52],"challenge":[53],"existing":[56],"feature":[57,103,120,138],"methods":[59],"terms":[61],"effectiveness":[63],"efficiency.":[65],"Hirschfeld-Gebelein-Renyi":[66],"maximal":[67],"correlation":[68,75],"is":[69,123],"effective":[71],"measure":[72],"between":[76,102],"variables.":[77],"In":[78],"this":[79,82],"paper,":[80],"with":[81,145],"measure,":[83],"we":[84],"proposed":[85,132],"improved":[87],"Network":[88],"Maximal":[89],"Correlation":[90],"(NMC)":[91],"model.":[92],"It":[93],"quickly":[95],"calculate":[98],"statistical":[100],"dependence":[101],"set":[104],"label":[106],"variable.":[107],"Further,":[108],"based":[109],"on":[110],"Recursive":[112],"Elimination":[114],"(RFE)":[115],"algorithm,":[116],"new":[118],"NMC-RFE":[119],"method":[122,133],"Further":[124],"proposed.":[125],"The":[126],"experimental":[127],"results":[128],"show":[129],"that":[130],"obtain":[135],"much":[136],"better":[137,150],"subsets":[139],"from":[140],"high":[141],"dimensional":[142],"sets":[144],"faster":[146],"calculation":[147],"speed":[148],"accuracy.":[151]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
