{"id":"https://openalex.org/W2096280253","doi":"https://doi.org/10.1109/ijcnn.2008.4633944","title":"Collaborative statistical learning with rough feature reduction for visual target classification","display_name":"Collaborative statistical learning with rough feature reduction for visual target classification","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2096280253","doi":"https://doi.org/10.1109/ijcnn.2008.4633944","mag":"2096280253"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2008.4633944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","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/A5031651152","display_name":"Sheng Wang","orcid":"https://orcid.org/0000-0002-2258-2633"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sheng Wang","raw_affiliation_strings":["State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","Dept. of Precision Instruments, Tsinghua Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Precision Instruments, Tsinghua Univ., Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035550613","display_name":"Xue Wang","orcid":"https://orcid.org/0000-0003-4842-3160"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Wang","raw_affiliation_strings":["State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","Dept. of Precision Instruments, Tsinghua Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Precision Instruments, Tsinghua Univ., Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031989161","display_name":"Daowei Bi","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daowei Bi","raw_affiliation_strings":["State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","Dept. of Precision Instruments, Tsinghua Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Precision Instruments, Tsinghua Univ., Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012020883","display_name":"Liang Ding","orcid":"https://orcid.org/0000-0002-8351-5178"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Ding","raw_affiliation_strings":["State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","Dept. of Precision Instruments, Tsinghua Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Precision Instruments, Tsinghua Univ., Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041300353","display_name":"Zheng You","orcid":"https://orcid.org/0000-0002-3941-1371"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng You","raw_affiliation_strings":["State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","Dept. of Precision Instruments, Tsinghua Univ., Beijing"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Precision Measurement Technology and Instrument, and the Department of Precision Instruments, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Precision Instruments, Tsinghua Univ., Beijing","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5031651152"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.3666,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67401026,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":null,"first_page":"1151","last_page":"1156"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9912999868392944,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.9822999835014343,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7835381031036377},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6623269319534302},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6590765118598938},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6240465641021729},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5267614126205444},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.5116012692451477},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5060457587242126},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4841111898422241},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.46208783984184265},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4555654525756836},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.44916093349456787},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.4216318130493164},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4035225510597229}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7835381031036377},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6623269319534302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6590765118598938},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6240465641021729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5267614126205444},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.5116012692451477},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5060457587242126},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4841111898422241},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.46208783984184265},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4555654525756836},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.44916093349456787},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.4216318130493164},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4035225510597229},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2008.4633944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1546507958","https://openalex.org/W1557923305","https://openalex.org/W1915454449","https://openalex.org/W2003251444","https://openalex.org/W2075818274","https://openalex.org/W2098093602","https://openalex.org/W2099527499","https://openalex.org/W2107008379","https://openalex.org/W2114701396","https://openalex.org/W2123033018","https://openalex.org/W2131620359","https://openalex.org/W2132641846","https://openalex.org/W2155763858","https://openalex.org/W2356481047","https://openalex.org/W6632860626","https://openalex.org/W6676132248","https://openalex.org/W6679469402"],"related_works":["https://openalex.org/W2040550925","https://openalex.org/W3112445271","https://openalex.org/W2368894008","https://openalex.org/W2134510694","https://openalex.org/W2109566635","https://openalex.org/W1964081096","https://openalex.org/W2141335490","https://openalex.org/W3139983161","https://openalex.org/W2169139688","https://openalex.org/W563719335"],"abstract_inverted_index":{"To":[0],"implement":[1,76,149],"visual":[2],"target":[3,29,150],"classification,":[4,30],"this":[5],"paper":[6],"proposes":[7],"a":[8,119],"collaborative":[9,143],"statistical":[10,144],"learning":[11,19,32,53,78,145],"algorithm":[12,74,146,163],"for":[13,38,95],"online":[14],"support":[15],"vector":[16],"machine(SVM)":[17],"classifier":[18,31,77],"in":[20,68,114,152,167],"wireless":[21],"multimedia":[22],"sensor":[23,85,116,120],"network":[24],"(WMSN).":[25],"For":[26],"achieving":[27],"robust":[28],"should":[33],"be":[34,50],"carried":[35],"out":[36],"iteratively":[37],"updating":[39],"classifiers":[40],"according":[41],"to":[42,56,64,107,127],"various":[43],"situations.":[44],"Because":[45],"only":[46],"unlabeled":[47,61],"samples":[48,113],"can":[49,147],"acquired,":[51],"semi-supervised":[52],"is":[54,93,124],"desired":[55],"make":[57],"full":[58],"use":[59],"of":[60,98,111,131],"samples.":[62],"According":[63],"the":[65,72,80,99,109,112,129,142,155,161],"restrict":[66],"limitation":[67],"energy":[69,168],"and":[70,118,135,170],"bandwidth,":[71],"proposed":[73,126,162],"incrementally":[75],"with":[79],"selected":[81],"features":[82],"from":[83],"multiple":[84],"nodes,":[86,117],"where":[87],"rough":[88,156],"set":[89,157],"based":[90,158],"feature":[91,159],"reduction":[92],"used":[94],"retaining":[96],"most":[97],"intrinsic":[100],"information.":[101],"Furthermore,":[102],"some":[103],"metrics":[104],"are":[105],"introduced":[106],"evaluate":[108],"effectiveness":[110],"specific":[115],"node":[121],"selection":[122],"strategy":[123],"also":[125],"reduce":[128],"impact":[130],"inevitable":[132],"missing":[133],"detection":[134],"false":[136],"detection.":[137],"Experimental":[138],"results":[139],"demonstrate":[140],"that":[141],"effectively":[148],"classification":[151],"WMSN.":[153],"With":[154],"reduction,":[160],"has":[164],"outstanding":[165],"performance":[166],"efficiency":[169],"time":[171],"cost.":[172]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}