{"id":"https://openalex.org/W2371610592","doi":null,"title":"A genetic algorithm based wrapper feature selection method for classification of hyper spectral data using support vector maching","display_name":"A genetic algorithm based wrapper feature selection method for classification of hyper spectral data using support vector maching","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2371610592","mag":"2371610592"},"language":"en","primary_location":{"id":"mag:2371610592","doi":null,"is_oa":false,"landing_page_url":"http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLYJ200803004.htm","pdf_url":null,"source":{"id":"https://openalex.org/S173349649","display_name":"Geographical Research","issn_l":"0004-9190","issn":["0004-9190","1467-8470","1745-5863","1745-5871"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Geographical Research","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5110369630","display_name":"Qian Jun-ping","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qian Jun-ping","raw_affiliation_strings":["School of Geographical Sciences, Guangzhou University,#N#Guangzhou, 510006, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geographical Sciences, Guangzhou University,#N#Guangzhou, 510006, China","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5110369630"],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":null,"fwci":0.6034,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.72508782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9818999767303467,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9818999767303467,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.007699999958276749,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.0034000000450760126,"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/wrapper-feature-selection","display_name":"wrapper-based feature selection","score":0.8270000219345093},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"genetic algorithm","score":0.9750000238418579},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.9950000047683716},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.8820000290870667},{"id":"https://openalex.org/keywords/hyperion","display_name":"Hyperion","score":0.8349999785423279}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7769752740859985},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7551850080490112},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5690057277679443},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5584474205970764},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5344281196594238},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5319830179214478},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5279290676116943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.496142715215683},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4187813997268677},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.41401034593582153},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3814893364906311},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3698612451553345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3251408338546753},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2984890043735504},{"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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2371610592","doi":null,"is_oa":false,"landing_page_url":"http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLYJ200803004.htm","pdf_url":null,"source":{"id":"https://openalex.org/S173349649","display_name":"Geographical Research","issn_l":"0004-9190","issn":["0004-9190","1467-8470","1745-5863","1745-5871"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Geographical Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2101246835","https://openalex.org/W2008997808","https://openalex.org/W3164307712","https://openalex.org/W1965407438","https://openalex.org/W2390510243","https://openalex.org/W1984180616","https://openalex.org/W1970932298","https://openalex.org/W2778999656","https://openalex.org/W2010479838","https://openalex.org/W1972650998"],"abstract_inverted_index":{"The":[0,53,67,142,210,255,277],"high-dimensional":[1,194],"feature":[2,37,45,75,168,195,203,309,315,330],"vectors":[3,196],"of":[4,19,44,73,101,119,140,155,159,173,197,206,231,257,280,332],"hyper":[5,120,174,198,227,334],"spectral":[6,121,175,199,228,335],"data":[7,122,176,200],"often":[8],"impose":[9],"a":[10,110,128,138,225],"high":[11,102],"computational":[12,103],"cost":[13,190,249],"as":[14,16,298],"well":[15],"the":[17,32,63,71,80,89,99,153,160,167,193,202,216,232,236,241,247,252,269,281,295,301,306,322,333],"risk":[18],"over":[20],"fitting":[21],"when":[22,201],"classification":[23,64,81,118,253,261,270],"is":[24,28],"performed.":[25],"Therefore":[26],"it":[27],"necessary":[29],"to":[30,134,148,164,187,266,275],"reduce":[31,188,246],"dimensionality":[33],"through":[34],"ways":[35],"like":[36],"selection.":[38],"Currently,":[39],"there":[40],"are":[41],"two":[42,282],"kinds":[43],"selection":[46,310,331],"methods:":[47],"filter":[48],"methods":[49,91,154],"and":[50,61,177,219,235,288,317],"wrapper":[51,90,115,308],"methods.":[52],"former":[54],"kind":[55,69],"requires":[56],"no":[57],"feedback":[58],"from":[59,264,273,294],"classifiers":[60],"estimates":[62],"performance":[65],"indirectly.":[66],"latter":[68],"evaluates":[70],"goodness":[72],"selected":[74],"subset":[76,204],"directly":[77],"based":[78,114],"on":[79],"accuracy.":[82,254],"Many":[83],"experimental":[84],"results":[85,234,238],"have":[86,98],"proved":[87],"that":[88,131,240],"can":[92,313,326],"yield":[93],"better":[94],"performance,":[95],"although":[96],"they":[97],"disadvantage":[100],"cost.":[104],"In":[105,304],"this":[106],"paper,":[107],"we":[108],"present":[109],"Genetic":[111],"Algorithm":[112],"(GA)":[113],"method":[116,212,243,311],"for":[117,260],"using":[123,152,215],"Support":[124],"Vector":[125],"Machine":[126],"(SVM),":[127],"state-of-art":[129],"classifier":[130],"has":[132],"found":[133],"be":[135,327],"success":[136],"in":[137,300,329],"variety":[139],"areas.":[141],"genetic":[143],"algorithm":[144],"(GA),":[145],"which":[146,291],"seeks":[147],"solve":[149],"optimization":[150],"problems":[151],"evolution,":[156],"specifically":[157],"survival":[158],"fittest,":[161],"was":[162,185,208,213,220,262],"used":[163,259,299],"optimize":[165,314],"both":[166],"subset,":[169,172],"i.e.":[170],"band":[171],"SVM":[178,283,318],"kernel":[179,284,319],"parameters":[180,285,320],"simultaneously.":[181],"A":[182],"special":[183],"strategy":[184],"adopted":[186],"computation":[189,248],"caused":[191],"by":[192,223],"part":[205],"chromosome":[207],"designed.":[209],"GA-SVM":[211,242,312],"realized":[214],"ENVI/IDL":[217],"language,":[218],"then":[221],"tested":[222],"applying":[224],"HYPERION":[226],"image.":[229],"Comparison":[230],"optimized":[233,278],"un-optimized":[237],"showed":[239],"could":[244],"significantly":[245],"while":[250,268],"improving":[251],"number":[256],"bands":[258],"reduced":[263],"198":[265],"13,":[267],"accuracy":[271],"increased":[272],"88.81%":[274],"92.51%.":[276],"values":[279,297],"were":[286,292],"95.0297":[287],"0.2021,":[289],"respectively,":[290],"different":[293],"default":[296],"ENVI":[302],"software.":[303],"conclusion,":[305],"proposed":[307],"subsets":[316],"at":[321],"same":[323],"time,":[324],"therefore":[325],"applied":[328],"data.":[336]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
