{"id":"https://openalex.org/W4387802078","doi":"https://doi.org/10.1109/igarss52108.2023.10281969","title":"Adaptive Cost Adjustment for SAR Imbalanced Classification via Reinforcement Learning","display_name":"Adaptive Cost Adjustment for SAR Imbalanced Classification via Reinforcement Learning","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387802078","doi":"https://doi.org/10.1109/igarss52108.2023.10281969"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10281969","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss52108.2023.10281969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International 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/A5101212586","display_name":"Jingqi Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingqi Wei","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061245960","display_name":"Zongyong Cui","orcid":"https://orcid.org/0000-0003-1155-786X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongyong Cui","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102883587","display_name":"Zheng Zhou","orcid":"https://orcid.org/0000-0001-5559-158X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Zhou","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019411747","display_name":"Zongjie Cao","orcid":"https://orcid.org/0000-0002-0117-9087"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongjie Cao","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076476995","display_name":"Yiming Pi","orcid":"https://orcid.org/0000-0002-5176-7901"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Pi","raw_affiliation_strings":["University of Electronic Science and Technology of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7515","last_page":"7518"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9775999784469604,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9578999876976013,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7230440974235535},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7226626873016357},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7071629762649536},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5938602685928345},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5796766877174377},{"id":"https://openalex.org/keywords/decision-boundary","display_name":"Decision boundary","score":0.5678975582122803},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5678617358207703},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5348320007324219},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.483146607875824},{"id":"https://openalex.org/keywords/one-class-classification","display_name":"One-class classification","score":0.46157726645469666},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.41561880707740784},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3221285343170166},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1814413070678711},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.16688108444213867}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7230440974235535},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7226626873016357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7071629762649536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5938602685928345},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5796766877174377},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.5678975582122803},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5678617358207703},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5348320007324219},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.483146607875824},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.46157726645469666},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.41561880707740784},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3221285343170166},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1814413070678711},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.16688108444213867}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10281969","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss52108.2023.10281969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.800000011920929}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W169052826","https://openalex.org/W1506588750","https://openalex.org/W2096945460","https://openalex.org/W2148143831","https://openalex.org/W2744139632","https://openalex.org/W2766296277","https://openalex.org/W2890551119","https://openalex.org/W2905236149","https://openalex.org/W2967029204","https://openalex.org/W3037958117","https://openalex.org/W3128898969"],"related_works":["https://openalex.org/W2004820431","https://openalex.org/W2137014120","https://openalex.org/W4376528628","https://openalex.org/W3134918839","https://openalex.org/W2900140651","https://openalex.org/W2134490011","https://openalex.org/W2914037311","https://openalex.org/W2560147515","https://openalex.org/W4205999209","https://openalex.org/W2444525338"],"abstract_inverted_index":{"Synthetic":[0],"aperture":[1],"radar":[2],"(SAR)":[3],"images":[4,13,72],"are":[5],"difficult":[6],"to":[7,58,98,133],"acquire,":[8],"and":[9,48,93,125,155,173],"the":[10,40,51,59,99,105,113,127,131,140,145,150,159],"number":[11,24,45],"of":[12,14,25,46,61,107,110,130,139,161,171],"different":[15],"targets":[16],"often":[17],"varies":[18],"greatly,":[19],"resulting":[20],"in":[21,29,70,82,177],"a":[22,43,90,120,135],"large":[23,44],"imbalanced":[26,68,162],"class":[27],"distributions":[28],"practical":[30],"applications.":[31],"Existing":[32],"classification":[33,62,69,101,138,166],"models":[34],"usually":[35],"focus":[36],"too":[37],"much":[38],"on":[39,50,74,144,168],"classes":[41,53,109],"with":[42,54],"samples":[47,111],"less":[49],"minority":[52,141],"higher-value,":[55],"which":[56],"leads":[57],"degradation":[60],"performance.":[63,179],"A":[64],"novel":[65],"method":[66,86,117,152],"for":[67],"SAR":[71],"based":[73],"reinforcement":[75],"learning":[76],"adaptive":[77],"cost":[78,91,123],"adjustment":[79],"is":[80,112],"proposed":[81,151],"this":[83],"paper.":[84],"The":[85],"can":[87,118],"adaptively":[88],"search":[89],"factor":[92,124],"adjust":[94],"it":[95],"continuously":[96],"according":[97],"predicted":[100],"effect":[102,160],"so":[103],"that":[104,149],"performance":[106],"all":[108,169],"best":[114],"possible.":[115],"This":[116],"obtain":[119],"more":[121,136],"accurate":[122,137],"modify":[126],"decision":[128],"boundary":[129],"classifier":[132],"achieve":[134],"classes.":[142],"Experiments":[143],"MSTAR":[146],"dataset":[147],"show":[148],"achieves":[153],"binary":[154],"multi-classification,":[156],"significantly":[157],"alleviates":[158],"data,":[163],"shows":[164],"better":[165],"results":[167],"kinds":[170],"samples,":[172],"outperforms":[174],"existing":[175],"methods":[176],"overall":[178]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}