{"id":"https://openalex.org/W2609580417","doi":"https://doi.org/10.1109/icpr.2016.7899635","title":"An empirical investigation into the inconsistency of sequential active learning","display_name":"An empirical investigation into the inconsistency of sequential active learning","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2609580417","doi":"https://doi.org/10.1109/icpr.2016.7899635","mag":"2609580417"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7899635","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899635","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","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/A5086060106","display_name":"Marco Loog","orcid":"https://orcid.org/0000-0002-1298-8461"},"institutions":[{"id":"https://openalex.org/I124055696","display_name":"University of Copenhagen","ror":"https://ror.org/035b05819","country_code":"DK","type":"education","lineage":["https://openalex.org/I124055696"]},{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["DK","NL"],"is_corresponding":true,"raw_author_name":"Marco Loog","raw_affiliation_strings":["Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands","The Image Section, University of Copenhagen, Copenhagen, Denmark"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]},{"raw_affiliation_string":"The Image Section, University of Copenhagen, Copenhagen, Denmark","institution_ids":["https://openalex.org/I124055696"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028219085","display_name":"Yazhou Yang","orcid":"https://orcid.org/0000-0002-1159-5682"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Yazhou Yang","raw_affiliation_strings":["Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5086060106"],"corresponding_institution_ids":["https://openalex.org/I124055696","https://openalex.org/I98358874"],"apc_list":null,"apc_paid":null,"fwci":1.7139,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.89855293,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"98","issue":null,"first_page":"210","last_page":"215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":1.0,"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":1.0,"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":0.9681000113487244,"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":0.9624999761581421,"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/computer-science","display_name":"Computer science","score":0.7378916144371033},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6856726408004761},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6590617299079895},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.6463860869407654},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6251842379570007},{"id":"https://openalex.org/keywords/learning-classifier-system","display_name":"Learning classifier system","score":0.464495450258255},{"id":"https://openalex.org/keywords/selection-bias","display_name":"Selection bias","score":0.44437992572784424},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.23947909474372864},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1474950909614563},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11926347017288208}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7378916144371033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6856726408004761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6590617299079895},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.6463860869407654},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6251842379570007},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.464495450258255},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.44437992572784424},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.23947909474372864},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1474950909614563},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11926347017288208}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2016.7899635","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899635","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W86235050","https://openalex.org/W1513874326","https://openalex.org/W1514940655","https://openalex.org/W1524761913","https://openalex.org/W1572210044","https://openalex.org/W1600399982","https://openalex.org/W1968200975","https://openalex.org/W2021367230","https://openalex.org/W2032536435","https://openalex.org/W2042932437","https://openalex.org/W2098158197","https://openalex.org/W2109033052","https://openalex.org/W2109059049","https://openalex.org/W2118585731","https://openalex.org/W2120708938","https://openalex.org/W2123610365","https://openalex.org/W2137959503","https://openalex.org/W2162651021","https://openalex.org/W2165484066","https://openalex.org/W2165698076","https://openalex.org/W2167595980","https://openalex.org/W2312804279","https://openalex.org/W2487277504","https://openalex.org/W2903158431","https://openalex.org/W3120740533","https://openalex.org/W6630965568","https://openalex.org/W6631546530","https://openalex.org/W6634233734","https://openalex.org/W6653695713","https://openalex.org/W6675921686","https://openalex.org/W6677656871"],"related_works":["https://openalex.org/W4206195464","https://openalex.org/W4364374183","https://openalex.org/W2917666173","https://openalex.org/W2156006853","https://openalex.org/W4306321456","https://openalex.org/W4388555444","https://openalex.org/W2961085424","https://openalex.org/W4285046548","https://openalex.org/W2090182691","https://openalex.org/W4213073923"],"abstract_inverted_index":{"In":[0,17,55],"active":[1,19,63,84,121,152],"learning,":[2,20,85,132],"one":[3],"aims":[4],"to":[5,106,131],"acquire":[6],"labeled":[7,50],"samples":[8],"that":[9,135],"are":[10],"particularly":[11],"useful":[12],"for":[13],"training":[14],"a":[15,27,143],"classifier.":[16],"sequential":[18],"this":[21,73,92,113],"sample":[22,34],"selection":[23],"is":[24],"done":[25],"in":[26,78,142],"one-at-a-time":[28],"manner":[29],"where":[30],"the":[31,41,45,48,79,86,98,108,118,149],"choice":[32],"of":[33,44,100,112,115,120,151],"t":[35,49],"+":[36],"1":[37],"may":[38],"depend":[39],"on":[40],"current":[42,62,125],"state":[43],"classifier":[46],"and":[47,103,110,140],"data":[51],"points":[52],"already":[53],"available.":[54],"their":[56,101],"deviation":[57],"from":[58],"standard":[59],"random":[60],"sampling,":[61],"learning":[64],"schemes":[65],"typically":[66],"introduce":[67],"severe":[68],"sampling":[69],"bias.":[70,93],"Even":[71,123],"though":[72,124],"fact":[74],"has":[75],"been":[76],"acknowledged":[77],"more":[80,87],"theoretical":[81],"contributions":[82],"covering":[83],"popular":[88],"approaches":[89,130],"largely":[90],"ignore":[91],"This":[94],"work":[95],"empirically":[96],"investigates":[97],"consequences":[99],"actions":[102],"sets":[104],"out":[105],"identify":[107],"pros":[109],"cons":[111],"way":[114],"dealing":[116],"with":[117],"problem":[119,150],"learning.":[122,153],"techniques":[126],"can":[127],"provide":[128,137],"excellent":[129],"we":[133],"conclude":[134],"they":[136],"inconsistent":[138],"solutions":[139],"therefore,":[141],"strict":[144],"sense,":[145],"do":[146],"not":[147],"solve":[148]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}