{"id":"https://openalex.org/W3161629990","doi":"https://doi.org/10.1109/euvip50544.2021.9483967","title":"Selective Probabilistic Classifier Based on Hypothesis Testing","display_name":"Selective Probabilistic Classifier Based on Hypothesis Testing","publication_year":2021,"publication_date":"2021-06-23","ids":{"openalex":"https://openalex.org/W3161629990","doi":"https://doi.org/10.1109/euvip50544.2021.9483967","mag":"3161629990"},"language":"en","primary_location":{"id":"doi:10.1109/euvip50544.2021.9483967","is_oa":false,"landing_page_url":"https://doi.org/10.1109/euvip50544.2021.9483967","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 9th European Workshop on Visual Information Processing (EUVIP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2105.03876","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075181996","display_name":"Saeed Bakhshi Germi","orcid":"https://orcid.org/0000-0003-3048-220X"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Saeed Bakhshi Germi","raw_affiliation_strings":["Tampere University, Tampere, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088180438","display_name":"Esa Rahtu","orcid":"https://orcid.org/0000-0001-8767-0864"},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Esa Rahtu","raw_affiliation_strings":["Tampere University, Tampere, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tampere University, Tampere, Finland","institution_ids":["https://openalex.org/I166825849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042399760","display_name":"Heikki Huttunen","orcid":"https://orcid.org/0000-0002-6571-0797"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heikki Huttunen","raw_affiliation_strings":["Visy Oy, Tampere, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visy Oy, Tampere, Finland","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04909091,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9991000294685364,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/softmax-function","display_name":"Softmax function","score":0.9064438343048096},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7421931028366089},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6234440207481384},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5958852767944336},{"id":"https://openalex.org/keywords/probabilistic-classification","display_name":"Probabilistic classification","score":0.5464259386062622},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4863170087337494},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4850398898124695},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.4488086700439453},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43774980306625366},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40638014674186707},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33943599462509155},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3227994441986084},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1978759765625},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19228774309158325},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.09133625030517578},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0872015655040741}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.9064438343048096},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7421931028366089},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6234440207481384},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5958852767944336},{"id":"https://openalex.org/C189119545","wikidata":"https://www.wikidata.org/wiki/Q5128022","display_name":"Probabilistic classification","level":4,"score":0.5464259386062622},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4863170087337494},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4850398898124695},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.4488086700439453},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43774980306625366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40638014674186707},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33943599462509155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3227994441986084},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1978759765625},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19228774309158325},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.09133625030517578},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0872015655040741},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/euvip50544.2021.9483967","is_oa":false,"landing_page_url":"https://doi.org/10.1109/euvip50544.2021.9483967","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 9th European Workshop on Visual Information Processing (EUVIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2105.03876","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.03876","pdf_url":"https://arxiv.org/pdf/2105.03876","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"pmh:oai:trepo.tuni.fi:10024/221096","is_oa":false,"landing_page_url":"https://trepo.tuni.fi/handle/10024/221096","pdf_url":null,"source":{"id":"https://openalex.org/S4306401860","display_name":"Tampere University Institutional Repository (Tampere University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I150589677","host_organization_name":"Tampere University of Applied Sciences","host_organization_lineage":["https://openalex.org/I150589677"],"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":"conference"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2105.03876","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.03876","pdf_url":"https://arxiv.org/pdf/2105.03876","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1861492603","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2194775991","https://openalex.org/W2272331516","https://openalex.org/W2560321925","https://openalex.org/W2567611697","https://openalex.org/W2618169590","https://openalex.org/W2752128027","https://openalex.org/W2752719613","https://openalex.org/W2767414122","https://openalex.org/W2767471303","https://openalex.org/W2785606994","https://openalex.org/W2889625178","https://openalex.org/W2963149653","https://openalex.org/W2963238274","https://openalex.org/W2963613748","https://openalex.org/W2963857521","https://openalex.org/W2963924212","https://openalex.org/W2964059111","https://openalex.org/W2980000166","https://openalex.org/W3006853338","https://openalex.org/W3032150799","https://openalex.org/W3036058780","https://openalex.org/W3090773703","https://openalex.org/W3118608800","https://openalex.org/W6617145748","https://openalex.org/W6639102338","https://openalex.org/W6730042731","https://openalex.org/W6738348428","https://openalex.org/W6745891213","https://openalex.org/W6747657013","https://openalex.org/W6779583367","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W3120400911","https://openalex.org/W2177788029","https://openalex.org/W2040801326","https://openalex.org/W1957831838","https://openalex.org/W4312017181","https://openalex.org/W4385147326","https://openalex.org/W2089923511","https://openalex.org/W2526168292","https://openalex.org/W1556281485","https://openalex.org/W2169073417"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,20,27,65,89,149,162,169],"simple":[6],"yet":[7],"effective":[8],"method":[9,63,106,122,140,159],"to":[10,25,38,81],"deal":[11],"with":[12,72,126,143],"the":[13,16,31,35,40,44,51,83,96,104,109,113,130,138,144,157,173],"violation":[14],"of":[15,85,88,112,129,137,165],"Closed-World":[17],"Assumption":[18],"for":[19,101],"classifier.":[21],"Previous":[22],"works":[23],"tend":[24],"apply":[26],"threshold":[28],"either":[29],"on":[30,69,125],"classification":[32],"scores":[33],"or":[34],"loss":[36],"function":[37],"reject":[39,117],"inputs":[41],"that":[42,156],"violate":[43],"assumption.":[45],"However,":[46],"these":[47],"methods":[48],"cannot":[49],"achieve":[50,161],"low":[52],"False":[53],"Positive":[54],"Ratio":[55],"(FPR)":[56],"required":[57],"in":[58],"safety":[59],"applications.":[60],"The":[61,120,135],"proposed":[62,105,121,139,158],"is":[64,79,141,148,154],"rejection":[66],"option":[67],"based":[68],"hypothesis":[70],"testing":[71],"probabilistic":[73,76],"networks.":[74],"With":[75],"networks,":[77],"it":[78],"possible":[80],"estimate":[82,108],"distribution":[84],"outcomes":[86],"instead":[87],"single":[90],"output.":[91],"By":[92],"utilizing":[93],"Z-test":[94],"over":[95],"mean":[97],"and":[98,116,132,167],"standard":[99],"deviation":[100],"each":[102],"class,":[103],"can":[107,160],"statistical":[110],"significance":[111],"network":[114],"certainty":[115],"uncertain":[118],"outputs.":[119],"was":[123],"experimented":[124],"different":[127],"configurations":[128],"COCO":[131],"CIFAR":[133],"datasets.":[134],"performance":[136],"compared":[142],"Softmax":[145],"Response,":[146],"which":[147],"known":[150],"top-performing":[151],"method.":[152],"It":[153],"shown":[155],"broader":[163],"range":[164],"operation":[166],"cover":[168],"lower":[170],"FPR":[171],"than":[172],"alternative.":[174]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2021-08-02T00:00:00"}
