{"id":"https://openalex.org/W3095513820","doi":"https://doi.org/10.3390/make2040028","title":"Towards Knowledge Uncertainty Estimation for Open Set Recognition","display_name":"Towards Knowledge Uncertainty Estimation for Open Set Recognition","publication_year":2020,"publication_date":"2020-10-30","ids":{"openalex":"https://openalex.org/W3095513820","doi":"https://doi.org/10.3390/make2040028","mag":"3095513820"},"language":"en","primary_location":{"id":"doi:10.3390/make2040028","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2040028","pdf_url":"https://www.mdpi.com/2504-4990/2/4/28/pdf?version=1604068595","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/2/4/28/pdf?version=1604068595","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010184065","display_name":"C.T.A. Pires","orcid":null},"institutions":[{"id":"https://openalex.org/I4210157425","display_name":"Fraunhofer Portugal Research","ror":"https://ror.org/05eqk2j25","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210157425","https://openalex.org/I4923324"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Catarina Pires","raw_affiliation_strings":["Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal","institution_ids":["https://openalex.org/I4210157425"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061814910","display_name":"Mar\u00edlia Barandas","orcid":"https://orcid.org/0000-0002-9445-4809"},"institutions":[{"id":"https://openalex.org/I4210157425","display_name":"Fraunhofer Portugal Research","ror":"https://ror.org/05eqk2j25","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210157425","https://openalex.org/I4923324"]},{"id":"https://openalex.org/I83558840","display_name":"Universidade Nova de Lisboa","ror":"https://ror.org/02xankh89","country_code":"PT","type":"education","lineage":["https://openalex.org/I83558840"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Mar\u00edlia Barandas","raw_affiliation_strings":["Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal","Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"],"raw_orcid":"https://orcid.org/0000-0002-9445-4809","affiliations":[{"raw_affiliation_string":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal","institution_ids":["https://openalex.org/I4210157425"]},{"raw_affiliation_string":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal","institution_ids":["https://openalex.org/I83558840"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021282797","display_name":"Let\u00edcia Fernandes","orcid":"https://orcid.org/0000-0002-1624-7716"},"institutions":[{"id":"https://openalex.org/I4210157425","display_name":"Fraunhofer Portugal Research","ror":"https://ror.org/05eqk2j25","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210157425","https://openalex.org/I4923324"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Let\u00edcia Fernandes","raw_affiliation_strings":["Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal"],"raw_orcid":"https://orcid.org/0000-0002-1624-7716","affiliations":[{"raw_affiliation_string":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal","institution_ids":["https://openalex.org/I4210157425"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080903555","display_name":"Duarte Folgado","orcid":"https://orcid.org/0000-0002-8481-6079"},"institutions":[{"id":"https://openalex.org/I4210157425","display_name":"Fraunhofer Portugal Research","ror":"https://ror.org/05eqk2j25","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210157425","https://openalex.org/I4923324"]},{"id":"https://openalex.org/I83558840","display_name":"Universidade Nova de Lisboa","ror":"https://ror.org/02xankh89","country_code":"PT","type":"education","lineage":["https://openalex.org/I83558840"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Duarte Folgado","raw_affiliation_strings":["Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal","Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"],"raw_orcid":"https://orcid.org/0000-0002-8481-6079","affiliations":[{"raw_affiliation_string":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal","institution_ids":["https://openalex.org/I4210157425"]},{"raw_affiliation_string":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal","institution_ids":["https://openalex.org/I83558840"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038205194","display_name":"Hugo Gamb\u00f4a","orcid":"https://orcid.org/0000-0002-4022-7424"},"institutions":[{"id":"https://openalex.org/I4210157425","display_name":"Fraunhofer Portugal Research","ror":"https://ror.org/05eqk2j25","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210157425","https://openalex.org/I4923324"]},{"id":"https://openalex.org/I83558840","display_name":"Universidade Nova de Lisboa","ror":"https://ror.org/02xankh89","country_code":"PT","type":"education","lineage":["https://openalex.org/I83558840"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Hugo Gamboa","raw_affiliation_strings":["Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal","Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal"],"raw_orcid":"https://orcid.org/0000-0002-4022-7424","affiliations":[{"raw_affiliation_string":"Associa\u00e7\u00e3o Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal","institution_ids":["https://openalex.org/I4210157425"]},{"raw_affiliation_string":"Laborat\u00f3rio de Instrumenta\u00e7\u00e3o, Engenharia Biom\u00e9dica e F\u00edsica da Radia\u00e7\u00e3o (LIBPhys-UNL), Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal","institution_ids":["https://openalex.org/I83558840"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5061814910"],"corresponding_institution_ids":["https://openalex.org/I4210157425","https://openalex.org/I83558840"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":1.224,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.84486094,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"2","issue":"4","first_page":"505","last_page":"532"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9976000189781189,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9922000169754028,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6325118541717529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6321552991867065},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6126483082771301},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5203642249107361},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.45923054218292236},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.4541882276535034},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.44745370745658875},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.4402335286140442},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4373796582221985},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32224327325820923},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27371275424957275},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13572287559509277}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6325118541717529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6321552991867065},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6126483082771301},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5203642249107361},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.45923054218292236},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4541882276535034},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.44745370745658875},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.4402335286140442},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4373796582221985},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32224327325820923},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27371275424957275},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13572287559509277},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.3390/make2040028","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2040028","pdf_url":"https://www.mdpi.com/2504-4990/2/4/28/pdf?version=1604068595","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6c2c05ad99db48e9be9d919967d0f1b0","is_oa":true,"landing_page_url":"https://doaj.org/article/6c2c05ad99db48e9be9d919967d0f1b0","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 2, Iss 4, Pp 505-532 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-4990/2/4/28/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/make2040028","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction; Volume 2; Issue 4; Pages: 505-532","raw_type":"Text"},{"id":"pmh:oai:publica.fraunhofer.de:publica/500885","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/500885","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"journal article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/502251","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/502251","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"journal article"},{"id":"pmh:oai:run.unl.pt:10362/144734","is_oa":true,"landing_page_url":"http://hdl.handle.net/10362/144734","pdf_url":null,"source":{"id":"https://openalex.org/S4306400678","display_name":"Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83558840","host_organization_name":"Universidade Nova de Lisboa","host_organization_lineage":["https://openalex.org/I83558840"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make2040028","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make2040028","pdf_url":"https://www.mdpi.com/2504-4990/2/4/28/pdf?version=1604068595","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4070304056","display_name":null,"funder_award_id":"POCI-01-0247-FEDER-033479","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"}],"funders":[{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3095513820.pdf","grobid_xml":"https://content.openalex.org/works/W3095513820.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W204885769","https://openalex.org/W1510526001","https://openalex.org/W1917989004","https://openalex.org/W1932198206","https://openalex.org/W1966716734","https://openalex.org/W1976526581","https://openalex.org/W1981401636","https://openalex.org/W1995443851","https://openalex.org/W2006715371","https://openalex.org/W2008513692","https://openalex.org/W2015563892","https://openalex.org/W2018459374","https://openalex.org/W2081337758","https://openalex.org/W2092509431","https://openalex.org/W2097863147","https://openalex.org/W2103568877","https://openalex.org/W2118377301","https://openalex.org/W2118680859","https://openalex.org/W2119880843","https://openalex.org/W2132870739","https://openalex.org/W2142047467","https://openalex.org/W2152052718","https://openalex.org/W2163288162","https://openalex.org/W2248269543","https://openalex.org/W2442806191","https://openalex.org/W2585399080","https://openalex.org/W2610309700","https://openalex.org/W2751802138","https://openalex.org/W2788755844","https://openalex.org/W2788907134","https://openalex.org/W2806471870","https://openalex.org/W2901114541","https://openalex.org/W2904422658","https://openalex.org/W2914619376","https://openalex.org/W2918105197","https://openalex.org/W2927351257","https://openalex.org/W2962689739","https://openalex.org/W2962842450","https://openalex.org/W2965989958","https://openalex.org/W2969609970","https://openalex.org/W2970946347","https://openalex.org/W2979544394","https://openalex.org/W2980423179","https://openalex.org/W2982437667","https://openalex.org/W2982482221","https://openalex.org/W2983826953","https://openalex.org/W3018928030","https://openalex.org/W3021421747","https://openalex.org/W3041751968","https://openalex.org/W3100410998","https://openalex.org/W3102616566","https://openalex.org/W3104422614","https://openalex.org/W6630424276","https://openalex.org/W6737164272","https://openalex.org/W6762924995"],"related_works":["https://openalex.org/W1991093342","https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2078622645","https://openalex.org/W2104657898","https://openalex.org/W2359563347","https://openalex.org/W3041490575","https://openalex.org/W2970690932","https://openalex.org/W4321135767","https://openalex.org/W2069619724"],"abstract_inverted_index":{"Uncertainty":[0,105],"is":[1,24,45,82,204],"ubiquitous":[2],"and":[3,17,48,62,69,113,120,123,148,157,171,180],"happens":[4],"in":[5,30,168,177,185],"every":[6],"single":[7],"prediction":[8],"of":[9,21,131,135,166,202,207],"Machine":[10,55],"Learning":[11,56],"models.":[12],"The":[13],"ability":[14],"to":[15,93,109],"estimate":[16],"quantify":[18,110,119],"the":[19,28,67,83,127,164],"uncertainty":[20,112,125,203,208],"individual":[22],"predictions":[23],"arguably":[25],"relevant,":[26],"all":[27,71],"more":[29,79],"safety-critical":[31],"applications.":[32],"Real-world":[33],"recognition":[34],"poses":[35],"multiple":[36],"challenges":[37],"since":[38],"a":[39,103,188,195],"model\u2019s":[40],"knowledge":[41,111],"about":[42],"physical":[43],"phenomenon":[44],"not":[46],"complete,":[47],"observations":[49],"are":[50,66,74],"incomplete":[51],"by":[52,133],"definition.":[53],"However,":[54],"algorithms":[57],"often":[58],"assume":[59],"that":[60,70],"train":[61],"test":[63],"data":[64,146],"distributions":[65],"same":[68],"testing":[72],"classes":[73,89],"present":[75],"during":[76,96],"training.":[77],"A":[78],"realistic":[80],"scenario":[81],"Open":[84],"Set":[85],"Recognition,":[86],"where":[87],"unknown":[88],"can":[90],"be":[91],"submitted":[92],"an":[94,205],"algorithm":[95],"testing.":[97],"In":[98],"this":[99],"paper,":[100],"we":[101,118],"propose":[102],"Knowledge":[104],"Estimation":[106],"(KUE)":[107],"method":[108],"reject":[114],"out-of-distribution":[115],"inputs.":[116],"Additionally,":[117],"distinguish":[121],"aleatoric":[122],"epistemic":[124],"with":[126,144,152,190,209],"classical":[128],"information-theoretical":[129],"measures":[130,201],"entropy":[132,161],"means":[134],"ensemble":[136,158],"techniques.":[137],"We":[138],"performed":[139],"experiments":[140],"on":[141,194],"four":[142],"datasets":[143],"different":[145,200],"modalities":[147],"compared":[149],"our":[150],"results":[151,176],"distance-based":[153],"classifiers,":[154],"SVM-based":[155],"approaches":[156],"techniques":[159],"using":[160],"measures.":[162],"Overall,":[163],"effectiveness":[165],"KUE":[167],"distinguishing":[169],"in-":[170],"out-distribution":[172],"inputs":[173],"obtained":[174],"better":[175],"most":[178],"cases":[179],"was":[181],"at":[182],"least":[183],"comparable":[184],"others.":[186],"Furthermore,":[187],"classification":[189],"rejection":[191],"option":[192],"based":[193],"proposed":[196],"combination":[197],"strategy":[198],"between":[199],"application":[206],"proven":[210],"results.":[211]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-15T08:27:34.491423","created_date":"2020-11-09T00:00:00"}