{"id":"https://openalex.org/W7092190396","doi":"https://doi.org/10.1109/lsp.2025.3622531","title":"FMAPLS: Bayesian Label Shift Estimation Based on Dynamic Dirichlet Parameter Adaptation","display_name":"FMAPLS: Bayesian Label Shift Estimation Based on Dynamic Dirichlet Parameter Adaptation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7092190396","doi":"https://doi.org/10.1109/lsp.2025.3622531"},"language":null,"primary_location":{"id":"doi:10.1109/lsp.2025.3622531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3622531","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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":"Jiawei Hu","orcid":"https://orcid.org/0009-0000-6237-2289"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jiawei Hu","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Imperial College London, London, U.K"],"raw_orcid":"https://orcid.org/0009-0000-6237-2289","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College London, London, U.K","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":null,"display_name":"Javier A. Barria","orcid":"https://orcid.org/0000-0003-4111-439X"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Javier A. Barria","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Imperial College London, London, U.K"],"raw_orcid":"https://orcid.org/0000-0003-4111-439X","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College London, London, U.K","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69799657,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":null,"first_page":"4074","last_page":"4078"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.4648999869823456,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.4648999869823456,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.14560000598430634,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.1080000028014183,"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/hyperparameter","display_name":"Hyperparameter","score":0.8902999758720398},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7911999821662903},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.5271000266075134},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4681999981403351},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4675999879837036},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4620000123977661},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.38370001316070557},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.36550000309944153},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.32919999957084656}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8902999758720398},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7911999821662903},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5861999988555908},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5296000242233276},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.5271000266075134},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4681999981403351},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4620000123977661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44690001010894775},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.38370001316070557},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35269999504089355},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3375999927520752},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.32919999957084656},{"id":"https://openalex.org/C141318989","wikidata":"https://www.wikidata.org/wiki/Q5753066","display_name":"Hierarchical Dirichlet process","level":4,"score":0.3253999948501587},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.3188999891281128},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.30820000171661377},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.30329999327659607},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C52290693","wikidata":"https://www.wikidata.org/wiki/Q5532435","display_name":"Generalized Dirichlet distribution","level":5,"score":0.265500009059906},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2639000117778778},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3622531","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3622531","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2028138594","https://openalex.org/W2092898962","https://openalex.org/W2141253686","https://openalex.org/W2160709761","https://openalex.org/W2583103953","https://openalex.org/W2797998707","https://openalex.org/W3121361649","https://openalex.org/W4391421140","https://openalex.org/W4393156830","https://openalex.org/W4394625553","https://openalex.org/W4404740476"],"related_works":[],"abstract_inverted_index":{"Label":[0,62,95],"shift,":[1,189],"a":[2,46,73,98,123],"critical":[3],"challenge":[4],"in":[5,27,35,48,133,173,186],"supervised":[6],"learning,":[7],"occurs":[8],"when":[9],"the":[10,180],"class":[11,110,193],"prior":[12,134],"distribution":[13],"of":[14,20,182],"test":[15,150],"data":[16],"deviates":[17],"from":[18],"that":[19,104],"training":[21],"data,":[22],"leading":[23],"to":[24,78,160,171],"significant":[25],"degradation":[26],"classifier":[28],"performance.":[29],"This":[30],"issue":[31],"is":[32],"particularly":[33,190],"impactful":[34],"real-world":[36],"applications":[37],"such":[38,57],"as":[39,58],"medical":[40],"diagnosis":[41],"and":[42,82,109,121,131,147,165,195],"satellite":[43],"imaging,":[44],"where":[45],"reduction":[47],"classification":[49],"accuracy":[50,167],"can":[51],"cause":[52],"severe":[53],"consequences.":[54],"Existing":[55],"methods,":[56],"Maximum":[59,92],"A":[60,93],"Posterior":[61,94],"Shift":[63,96],"(MAPLS),":[64],"rely":[65],"on":[66,75,142],"strict":[67],"Dirichlet":[68,107,148],"hyperparameter":[69,119,184],"constraints,":[70],"thereby":[71],"imposing":[72],"limitation":[74],"their":[76],"adaptability":[77],"dynamic":[79,183],"learning":[80],"environments":[81],"class-imbalanced":[83],"scenarios.":[84],"To":[85],"address":[86],"this":[87],"problem,":[88],"we":[89],"propose":[90],"Full":[91],"(FMAPLS),":[97],"Bayesian":[99],"label":[100,188],"shift":[101],"estimation":[102,135],"framework":[103],"dynamically":[105],"co-optimizes":[106],"hyperparameters":[108],"priors":[111,151],"through":[112],"Expectation-Maximization":[113],"(EM)":[114],"iterations.":[115],"By":[116],"eliminating":[117],"rigid":[118],"constraints":[120],"introducing":[122],"linear":[124],"surrogate":[125],"function,":[126],"FMAPLS":[127],"enhances":[128],"both":[129],"expressivity":[130],"robustness":[132],"while":[136],"reducing":[137],"computational":[138],"complexity.":[139],"Extensive":[140],"experiments":[141],"CIFAR100":[143],"under":[144,191],"shuffled":[145],"long-tail":[146],"imbalanced":[149],"demonstrate":[152],"FMAPLS's":[153],"superiority":[154],"over":[155],"state-of-the-art":[156],"baselines,":[157],"achieving":[158],"up":[159,170],"50%":[161],"lower":[162],"KL":[163],"divergence":[164],"maintaining":[166],"improvements":[168],"by":[169],"0.5%":[172],"extreme":[174],"imbalance":[175,194],"settings.":[176],"The":[177],"results":[178],"validate":[179],"efficacy":[181],"adaptation":[185],"estimating":[187],"high":[192],"distributional":[196],"uncertainty.":[197]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-17T00:00:00"}
