{"id":"https://openalex.org/W2052421102","doi":"https://doi.org/10.1109/igarss.2014.6947223","title":"Coalition game theory based feature subset selection for hyperspectral image classification","display_name":"Coalition game theory based feature subset selection for hyperspectral image classification","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W2052421102","doi":"https://doi.org/10.1109/igarss.2014.6947223","mag":"2052421102"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2014.6947223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2014.6947223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5110051383","display_name":"Prudhvi Gurram","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prudhvi Gurram","raw_affiliation_strings":["MBO Partners Inc., Adelphi, MD","MBO Partners Inc., C/O ARL, RDRL-SES-E, 2800 Powder Mill Rd, Adelphi MD 20783"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MBO Partners Inc., Adelphi, MD","institution_ids":[]},{"raw_affiliation_string":"MBO Partners Inc., C/O ARL, RDRL-SES-E, 2800 Powder Mill Rd, Adelphi MD 20783","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040282679","display_name":"Heesung Kwon","orcid":null},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heesung Kwon","raw_affiliation_strings":["ATTN: RDRL-SES-E, Army Research Laboratory, MD","Army Research Laboratory, ATTN: RDRL-SES-E, 2800 Powder Mill Rd, Adelphi MD 20783"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ATTN: RDRL-SES-E, Army Research Laboratory, MD","institution_ids":["https://openalex.org/I166416128"]},{"raw_affiliation_string":"Army Research Laboratory, ATTN: RDRL-SES-E, 2800 Powder Mill Rd, Adelphi MD 20783","institution_ids":["https://openalex.org/I166416128"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0788,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.83479781,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3446","last_page":"3449"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9961000084877014,"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/T10057","display_name":"Face and Expression Recognition","score":0.9961000084877014,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12676","display_name":"Machine Learning and ELM","score":0.9803000092506409,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7305129766464233},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.676311194896698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5696136951446533},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5518336296081543},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4991800785064697},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4895516335964203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46559226512908936},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4606480896472931},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45816877484321594},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.42219147086143494},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.4218355715274811},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.41407907009124756},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.33777183294296265},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2874184846878052}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7305129766464233},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.676311194896698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5696136951446533},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5518336296081543},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4991800785064697},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4895516335964203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46559226512908936},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4606480896472931},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45816877484321594},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.42219147086143494},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.4218355715274811},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.41407907009124756},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.33777183294296265},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2874184846878052},{"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/igarss.2014.6947223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2014.6947223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"},{"id":"https://openalex.org/F4320338314","display_name":"Edgewood Chemical Biological Center","ror":"https://ror.org/02rdkx920"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W179492584","https://openalex.org/W2017337590","https://openalex.org/W2108710077","https://openalex.org/W2115465207","https://openalex.org/W2150480892","https://openalex.org/W2160540263","https://openalex.org/W3007590609","https://openalex.org/W6607266775","https://openalex.org/W6677400474","https://openalex.org/W6682385587","https://openalex.org/W6683864942"],"related_works":["https://openalex.org/W2166595269","https://openalex.org/W2725311638","https://openalex.org/W2123146423","https://openalex.org/W2091950550","https://openalex.org/W2570832236","https://openalex.org/W2144299089","https://openalex.org/W2133046540","https://openalex.org/W3092342941","https://openalex.org/W4388570703","https://openalex.org/W2155561932"],"abstract_inverted_index":{"In":[0],"this":[1,48],"paper,":[2],"an":[3],"algorithm":[4,114,158],"to":[5,69,109,128,151],"select":[6],"feature":[7,23],"subsets":[8,86,119,133],"for":[9],"hyper-spectral":[10],"image":[11],"classification":[12,80],"using":[13],"the":[14,54,71,79,99,110,118,129,166,171],"principle":[15],"of":[16,56,73,76,87,93,120,134,145],"coalition":[17,57],"game":[18,58],"theory":[19,59],"is":[20,67,115,159],"presented.":[21],"The":[22,113,131,157],"selection":[24],"algorithms":[25],"associated":[26],"with":[27,47,83],"non-linear":[28],"kernel":[29],"based":[30,52],"Support":[31],"Vector":[32],"Machines":[33],"(SVM)":[34],"are":[35,136,148,168],"either":[36],"NP-hard":[37],"or":[38],"greedy":[39],"and":[40,63,142,165],"hence,":[41],"not":[42,123],"very":[43],"optimal.":[44],"To":[45],"deal":[46],"problem,":[49],"a":[50,64,74,84,105,153],"metric":[51],"on":[53,161],"principles":[55],"called":[60],"Shapely":[61],"value":[62],"sampling":[65],"approximation":[66],"used":[68,138],"determine":[70],"contribution":[72],"subset":[75],"features":[77,121,135],"towards":[78],"task.":[81],"Starting":[82],"few":[85],"features,":[88],"we":[89],"successively":[90],"partition":[91],"each":[92],"them":[94],"into":[95],"smaller":[96,100],"parts":[97,101],"if":[98],"contribute":[102],"more":[103],"than":[104],"pre-determined":[106],"threshold":[107],"compared":[108],"parent":[111],"subset.":[112],"terminated":[116],"when":[117],"do":[122],"change":[124],"from":[125],"one":[126],"iteration":[127],"next.":[130],"final":[132],"then":[137],"in":[139,170],"multiple":[140],"kernels":[141,147],"sparse":[143],"weights":[144],"these":[146],"optimally":[149],"learned":[150],"build":[152],"maximum":[154],"margin":[155],"classifier.":[156],"applied":[160],"real":[162],"hyperspectral":[163],"datasets":[164],"results":[167],"presented":[169],"paper.":[172]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}