{"id":"https://openalex.org/W6922382124","doi":"https://doi.org/10.1109/tetci.2025.3582842","title":"Semi-Supervised Hyperspectral Adaptive Band Selection Based on Evolutionary Multitasking","display_name":"Semi-Supervised Hyperspectral Adaptive Band Selection Based on Evolutionary Multitasking","publication_year":2025,"publication_date":"2025-07-10","ids":{"openalex":"https://openalex.org/W6922382124","doi":"https://doi.org/10.1109/tetci.2025.3582842"},"language":"en","primary_location":{"id":"doi:10.1109/tetci.2025.3582842","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2025.3582842","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-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":null,"display_name":"Jiao Shi","orcid":"https://orcid.org/0000-0003-1124-6738"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiao Shi","raw_affiliation_strings":["Research and Development Institute, Northwestern Polytechnical University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-1124-6738","affiliations":[{"raw_affiliation_string":"Research and Development Institute, Northwestern Polytechnical University, Shenzhen, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nan Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Zhang","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yu Lei","orcid":"https://orcid.org/0000-0001-9892-4460"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Lei","raw_affiliation_strings":["Research and Development Institute, Northwestern Polytechnical University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-9892-4460","affiliations":[{"raw_affiliation_string":"Research and Development Institute, Northwestern Polytechnical University, Shenzhen, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":null,"display_name":"A. K. Qin","orcid":"https://orcid.org/0000-0001-6631-1651"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"A. K. Qin","raw_affiliation_strings":["Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0001-6631-1651","affiliations":[{"raw_affiliation_string":"Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia","institution_ids":["https://openalex.org/I57093077"]}]},{"author_position":"last","author":{"id":null,"display_name":"Maoguo Gong","orcid":"https://orcid.org/0000-0002-0415-8556"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maoguo Gong","raw_affiliation_strings":["School of Electronic Engineering, Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-0415-8556","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.762,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.80443714,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"10","issue":"2","first_page":"1878","last_page":"1891"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9894000291824341,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9894000291824341,"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/T10057","display_name":"Face and Expression Recognition","score":0.0017000000225380063,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/human-multitasking","display_name":"Human multitasking","score":0.7789000272750854},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5899999737739563},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5782999992370605},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5723000168800354},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5389000177383423},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.48559999465942383},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.48249998688697815},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47510001063346863},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.4690999984741211}],"concepts":[{"id":"https://openalex.org/C107418235","wikidata":"https://www.wikidata.org/wiki/Q1520565","display_name":"Human multitasking","level":2,"score":0.7789000272750854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.692799985408783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6586999893188477},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5899999737739563},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5782999992370605},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5723000168800354},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5389000177383423},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5001999735832214},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.48559999465942383},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.48249998688697815},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.4690999984741211},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.38929998874664307},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3310000002384186},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.27790001034736633},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.26100000739097595}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tetci.2025.3582842","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2025.3582842","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7888469696044922}],"awards":[{"id":"https://openalex.org/G7609096038","display_name":null,"funder_award_id":"62472356","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Band":[0],"selection":[1],"(BS)":[2],"is":[3,73,108,150,170],"commonly":[4],"utilized":[5],"to":[6,35,63,136,152,158,172],"reduce":[7],"the":[8,37,44,66,96,112,121,131,138,142,188,193],"dimensionality":[9],"of":[10,48,99,117,197],"hyperspectral":[11],"images":[12],"(HSI).":[13],"Some":[14],"semi-supervised":[15,55],"BS":[16,57,71,82,101],"methods":[17,28],"achieve":[18,95],"great":[19],"classification":[20],"performance":[21],"using":[22],"limited":[23],"labeled":[24],"samples.":[25],"However,":[26],"most":[27],"rely":[29],"on":[30,60,90],"a":[31,54,76,103,166],"unified":[32],"discriminant":[33,92,202],"learner":[34],"find":[36],"band":[38,87],"subset,":[39],"which":[40,110],"lacks":[41],"consideration":[42],"for":[43],"complex":[45,194],"feature":[46,195],"representation":[47,196],"HSI":[49,198],"data.":[50],"This":[51],"paper":[52],"proposes":[53],"adaptive":[56],"method":[58,190],"based":[59,89],"evolutionary":[61,105],"multitasking":[62,106],"effectively":[64,191],"explore":[65],"diverse":[67],"intrinsic":[68],"characteristics.":[69],"The":[70],"problem":[72,79],"modeled":[74],"as":[75],"multitask":[77],"optimization":[78],"where":[80],"multiple":[81],"tasks":[83],"jointly":[84],"select":[85],"optimal":[86],"subset":[88],"different":[91],"learners.":[93,203],"To":[94],"collaborative":[97],"search":[98],"these":[100],"tasks,":[102],"structure-aware":[104],"approach":[107],"proposed,":[109],"generates":[111],"individuals":[113],"with":[114],"various":[115],"number":[116],"bands":[118],"by":[119,177],"considering":[120],"low":[122],"redundancy":[123],"and":[124,129,181],"complete":[125],"spectral":[126],"structure":[127,143],"information,":[128],"leverages":[130],"specially":[132],"designed":[133],"genetic":[134],"operations":[135],"facilitate":[137],"population":[139],"evolution":[140],"under":[141],"preservation":[144],"constraint.":[145],"A":[146],"dynamic":[147],"interaction":[148,155],"mechanism":[149],"devised":[151],"adjust":[153],"task":[154],"intensity":[156],"adaptively":[157],"enhance":[159],"effective":[160],"knowledge":[161],"sharing":[162],"between":[163],"tasks.":[164],"Additionally,":[165],"discriminability":[167],"evaluation":[168],"criterion":[169],"proposed":[171,189],"mitigate":[173],"noisy":[174],"sample":[175,179],"interference":[176],"integrating":[178],"reliability":[180],"class":[182],"separability.":[183],"Experiments":[184],"have":[185],"demonstrated":[186],"that":[187],"explores":[192],"data":[199],"through":[200],"joint":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}