{"id":"https://openalex.org/W2152221710","doi":"https://doi.org/10.1109/ijcnn.2008.4633815","title":"Active Meta-Learning with Uncertainty Sampling and Outlier Detection","display_name":"Active Meta-Learning with Uncertainty Sampling and Outlier Detection","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2152221710","doi":"https://doi.org/10.1109/ijcnn.2008.4633815","mag":"2152221710"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2008.4633815","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-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":"https://openalex.org/A5083998049","display_name":"Ricardo B. C. Prud\u00eancio","orcid":"https://orcid.org/0000-0001-7084-1233"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Ricardo B. C. Prudencio","raw_affiliation_strings":["Department of Information Science, Federal University of Pernambuco (UFPE), Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Information Science, Federal University of Pernambuco (UFPE), Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025550530","display_name":"Teresa B. Ludermir","orcid":"https://orcid.org/0000-0002-8980-6742"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Teresa B. Ludermir","raw_affiliation_strings":["Center of Informatic, Federal University of Pernambuco (UFPE), Brazil"],"affiliations":[{"raw_affiliation_string":"Center of Informatic, Federal University of Pernambuco (UFPE), Brazil","institution_ids":["https://openalex.org/I25112270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083998049"],"corresponding_institution_ids":["https://openalex.org/I25112270"],"apc_list":null,"apc_paid":null,"fwci":0.5866,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.80056376,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"2","issue":null,"first_page":"346","last_page":"351"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9983999729156494,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9983999729156494,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.998199999332428,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9872000217437744,"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/meta-learning","display_name":"Meta learning (computer science)","score":0.8254909515380859},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.770060658454895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6688559055328369},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6562052965164185},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6247244477272034},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5348884463310242},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.47724461555480957},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4694214463233948},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.452007919549942},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3455145061016083},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10005903244018555},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.09338343143463135}],"concepts":[{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.8254909515380859},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.770060658454895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6688559055328369},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6562052965164185},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6247244477272034},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5348884463310242},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.47724461555480957},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4694214463233948},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.452007919549942},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3455145061016083},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10005903244018555},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.09338343143463135},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2008.4633815","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.147.1621","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1621","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cin.ufpe.br/~rbcp/papers/ijcnn-2008-final.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.49000000953674316}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1484084878","https://openalex.org/W1494580925","https://openalex.org/W1520531476","https://openalex.org/W1595526332","https://openalex.org/W1979412670","https://openalex.org/W1985789779","https://openalex.org/W2037322594","https://openalex.org/W2038181716","https://openalex.org/W2080021732","https://openalex.org/W2085989833","https://openalex.org/W2098203240","https://openalex.org/W2108626634","https://openalex.org/W2117985513","https://openalex.org/W2125543909","https://openalex.org/W2132213912","https://openalex.org/W2141408223","https://openalex.org/W2142334564","https://openalex.org/W2145680191","https://openalex.org/W2151023586","https://openalex.org/W2166353350","https://openalex.org/W2167467747","https://openalex.org/W2171332245","https://openalex.org/W2951911250","https://openalex.org/W4230030242","https://openalex.org/W4285719527","https://openalex.org/W6628826841","https://openalex.org/W6629689645","https://openalex.org/W6678723600"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2091347716","https://openalex.org/W98577079","https://openalex.org/W4206195464","https://openalex.org/W4319309271","https://openalex.org/W4388481789","https://openalex.org/W2548988175","https://openalex.org/W2985282780","https://openalex.org/W4288086752","https://openalex.org/W2996551567"],"abstract_inverted_index":{"Meta-Learning":[0,81,107,129],"has":[1,82],"been":[2,83],"used":[3],"to":[4,71,85],"predict":[5],"the":[6,16,29,38,43,77,92,97,128],"performance":[7,40],"of":[8,15,31,51,55,76],"learning":[9,17],"algorithms":[10,45],"based":[11],"on":[12,46],"descriptive":[13],"features":[14,30],"problems.":[18],"Each":[19],"training":[20],"example":[21],"in":[22,96,120,127],"this":[23,87,101],"context,":[24],"i.e.":[25],"each":[26,62],"meta-example,":[27],"stores":[28],"a":[32,53,121],"given":[33],"problem":[34,63],"and":[35,113],"information":[36],"about":[37],"empirical":[39,74],"obtained":[41],"by":[42,89],"candidate":[44,78],"that":[47],"problem.":[48],"The":[49],"process":[50],"constructing":[52],"set":[54],"meta-examples":[56],"may":[57],"be":[58],"expensive,":[59],"since":[60],"for":[61,65],"avaliable":[64],"meta-example":[66,98],"generation,":[67],"it":[68],"is":[69],"necessary":[70],"perform":[72],"an":[73,105],"evaluation":[75],"algorithms.":[79],"Active":[80,106],"proposed":[84,104],"overcome":[86],"limitation":[88],"selecting":[90],"only":[91],"most":[93],"informative":[94],"problems":[95],"generation.":[99],"In":[100],"work,":[102],"we":[103],"method":[108],"which":[109],"combines":[110],"Uncertainty":[111],"Sampling":[112],"Outlier":[114],"Detection":[115],"techniques.":[116],"Experiments":[117],"were":[118],"performed":[119],"case":[122],"study,":[123],"yielding":[124],"significant":[125],"improvement":[126],"performance.":[130]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
