{"id":"https://openalex.org/W3153511226","doi":"https://doi.org/10.1007/s00521-021-06137-w","title":"Detecting and learning from unknown by extremely weak supervision: exploratory classifier (xClass)","display_name":"Detecting and learning from unknown by extremely weak supervision: exploratory classifier (xClass)","publication_year":2021,"publication_date":"2021-06-06","ids":{"openalex":"https://openalex.org/W3153511226","doi":"https://doi.org/10.1007/s00521-021-06137-w","mag":"3153511226"},"language":"en","primary_location":{"id":"doi:10.1007/s00521-021-06137-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-021-06137-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-021-06137-w.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00521-021-06137-w.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039480864","display_name":"Plamen Angelov","orcid":"https://orcid.org/0000-0002-5770-934X"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Plamen Angelov","raw_affiliation_strings":["School of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068590850","display_name":"Eduardo Soares","orcid":"https://orcid.org/0000-0002-2634-8270"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Eduardo Soares","raw_affiliation_strings":["School of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, UK"],"raw_orcid":"https://orcid.org/0000-0002-2634-8270","affiliations":[{"raw_affiliation_string":"School of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, UK","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068590850"],"corresponding_institution_ids":["https://openalex.org/I67415387"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.5596,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.72363946,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"33","issue":"22","first_page":"15145","last_page":"15157"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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":0.9983000159263611,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9965999722480774,"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":0.7952313423156738},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6856901049613953},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6635497212409973},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6185309290885925},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4727407693862915},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4410059154033661},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.43145155906677246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7952313423156738},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6856901049613953},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6635497212409973},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6185309290885925},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4727407693862915},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4410059154033661},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.43145155906677246}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s00521-021-06137-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-021-06137-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-021-06137-w.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},{"id":"pmh:oai:eprints.lancs.ac.uk:154978","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.lancs.ac.uk/id/eprint/154978/1/Detecting_and_Learning_from_Unknown_Neural_Computing_8_.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"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":"PeerReviewed"}],"best_oa_location":{"id":"doi:10.1007/s00521-021-06137-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00521-021-06137-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-021-06137-w.pdf","source":{"id":"https://openalex.org/S147897268","display_name":"Neural Computing and Applications","issn_l":"0941-0643","issn":["0941-0643","1433-3058"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computing and Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3153511226.pdf","grobid_xml":"https://content.openalex.org/works/W3153511226.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W43637044","https://openalex.org/W602163042","https://openalex.org/W648613739","https://openalex.org/W1472232700","https://openalex.org/W1669383326","https://openalex.org/W1961147827","https://openalex.org/W2000343360","https://openalex.org/W2048430744","https://openalex.org/W2060455590","https://openalex.org/W2076063813","https://openalex.org/W2092924973","https://openalex.org/W2095852873","https://openalex.org/W2095927570","https://openalex.org/W2113362740","https://openalex.org/W2119112357","https://openalex.org/W2119717791","https://openalex.org/W2134270519","https://openalex.org/W2152161790","https://openalex.org/W2166049352","https://openalex.org/W2171253128","https://openalex.org/W2396069607","https://openalex.org/W2404399993","https://openalex.org/W2465549242","https://openalex.org/W2529546255","https://openalex.org/W2754458851","https://openalex.org/W2766468550","https://openalex.org/W2894880167","https://openalex.org/W2896732351","https://openalex.org/W2899519211","https://openalex.org/W2919115771","https://openalex.org/W2929291387","https://openalex.org/W2929786138","https://openalex.org/W2945976633","https://openalex.org/W3033988828","https://openalex.org/W3041378136","https://openalex.org/W3217380390","https://openalex.org/W4250859275"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4224009465","https://openalex.org/W4306674287","https://openalex.org/W2049864679","https://openalex.org/W4292388283","https://openalex.org/W3204418343","https://openalex.org/W1560624709","https://openalex.org/W3166286441","https://openalex.org/W3214142563","https://openalex.org/W3111760155"],"abstract_inverted_index":{"Abstract":[0],"In":[1,102],"this":[2,72],"paper,":[3],"we":[4,154,185,203,219],"break":[5],"with":[6,223],"the":[7,61,76,80,84,90,94,104,122,125,148,192,215,237],"traditional":[8],"approach":[9],"to":[10,69,83,110,239],"classification,":[11],"which":[12,27,120,173],"is":[13,108,113,174],"regarded":[14],"as":[15],"a":[16,23,40,57,129,131,175,206],"form":[17],"of":[18,34,124,177,179,191,226],"supervised":[19],"learning.":[20],"We":[21,235],"offer":[22],"method":[24,64],"and":[25,37,60,65,73,112,116,170,218,232,241,248],"algorithm,":[26],"make":[28],"possible":[29],"fully":[30],"autonomous":[31,180],"(unsupervised)":[32],"detection":[33],"new":[35,51,243],"classes,":[36],"learning":[38],"following":[39],"very":[41],"parsimonious":[42,144],"training":[43],"priming":[44],"(few":[45],"labeled":[46],"data":[47,77,96,126,164],"samples":[48],"only).":[49],"Moreover,":[50],"unknown":[52],"classes":[53,217,244],"may":[54],"appear":[55],"at":[56],"later":[58],"stage":[59],"proposed":[62],"xClass":[63],"algorithm":[66,91],"are":[67,86],"able":[68],"successfully":[70],"discover":[71],"learn":[74,242],"from":[75,147],"autonomously.":[78],"Furthermore,":[79],"features":[81,227],"(inputs":[82],"classifier)":[85],"automatically":[87,105],"sub-selected":[88],"by":[89,195],"based":[92,117],"on":[93,118,157],"accumulated":[95],"density":[97],"per":[98,100],"feature":[99],"class.":[101],"addition,":[103],"generated":[106,220],"model":[107,138],"easy":[109],"interpret":[111],"locally":[114],"generative":[115],"prototypes":[119],"define":[121],"modes":[123],"distribution.":[127],"As":[128],"result,":[130],"highly":[132],"efficient,":[133],"lean,":[134],"human-understandable,":[135],"autonomously":[136],"self-learning":[137],"(which":[139],"only":[140,184,204],"needs":[141],"an":[142],"extremely":[143,230],"priming)":[145],"emerges":[146],"data.":[149],"To":[150],"validate":[151],"our":[152],"proposal,":[153],"approbated":[155],"it":[156],"four":[158],"challenging":[159],"problems,":[160],"including":[161],"imbalanced":[162],"Faces-1999":[163],"base,":[165],"Caltech-101":[166],"dataset,":[167,169,172],"vehicles":[168],"iRoads":[171],"dataset":[176],"images":[178,247],"driving":[181],"scenarios.":[182],"Not":[183],"achieved":[186],"higher":[187],"precision":[188],"(in":[189],"one":[190],"problems":[193],"outperforming":[194],"25%":[196],"all":[197,214],"other":[198,211],"methods),":[199],"but,":[200],"more":[201],"significantly,":[202],"used":[205,213],"single":[207],"class":[208],"beforehand,":[209],"while":[210],"methods":[212],"available":[216],"interpretable":[221],"models":[222],"smaller":[224],"number":[225],"used,":[228],"through":[229],"weak":[231,233],"supervision.":[234],"demonstrated":[236],"ability":[238],"detect":[240],"for":[245],"both":[246],"numerical":[249],"examples.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2025-10-10T00:00:00"}
