{"id":"https://openalex.org/W2361043606","doi":null,"title":"AN ACTIVE BAYESIAN NETWORK CLASSIFIER","display_name":"AN ACTIVE BAYESIAN NETWORK CLASSIFIER","publication_year":2002,"publication_date":"2002-01-01","ids":{"openalex":"https://openalex.org/W2361043606","mag":"2361043606"},"language":"en","primary_location":{"id":"mag:2361043606","doi":null,"is_oa":false,"landing_page_url":"http://en.cnki.com.cn/Article_en/CJFDTOTAL-JFYZ200205010.htm","pdf_url":null,"source":{"id":"https://openalex.org/S4210240373","display_name":"Journal of Computer Research and Development","issn_l":"1000-1239","issn":["1000-1239"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310319982","host_organization_name":"Science Press","host_organization_lineage":["https://openalex.org/P4310319982"],"host_organization_lineage_names":["Science Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Computer Research and Development","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5044229774","display_name":"Gong Xiu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gong Xiu","raw_affiliation_strings":["Jun, SUN Jian  Ping, and SHI Zhong  Zhi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jun, SUN Jian  Ping, and SHI Zhong  Zhi","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5044229774"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9940999746322632,"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":0.9940999746322632,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.00419999985024333,"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":0.00019999999494757503,"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/active-learning-machine-learning","display_name":"active learning (machine learning)","score":0.9990000128746033},{"id":"https://openalex.org/keywords/bayesian-network-classifiers","display_name":"Bayesian network classifiers","score":0.7350000143051147},{"id":"https://openalex.org/keywords/entropy-maximization","display_name":"entropy maximization","score":0.7480000257492065},{"id":"https://openalex.org/keywords/entropy-minimization","display_name":"entropy minimization","score":0.9879999756813049},{"id":"https://openalex.org/keywords/uncertainty-sampling","display_name":"uncertainty sampling","score":0.9900000095367432},{"id":"https://openalex.org/keywords/incremental-model-updating","display_name":"incremental model updating","score":0.699999988079071},{"id":"https://openalex.org/keywords/sample-selection-strategy","display_name":"sample selection strategy","score":0.5479999780654907}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8566517233848572},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.7217843532562256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7036097049713135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6793484687805176},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.508995771408081},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49192357063293457},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.45087170600891113},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4451945722103119},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44351261854171753},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.327747106552124},{"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":1,"locations":[{"id":"mag:2361043606","doi":null,"is_oa":false,"landing_page_url":"http://en.cnki.com.cn/Article_en/CJFDTOTAL-JFYZ200205010.htm","pdf_url":null,"source":{"id":"https://openalex.org/S4210240373","display_name":"Journal of Computer Research and Development","issn_l":"1000-1239","issn":["1000-1239"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310319982","host_organization_name":"Science Press","host_organization_lineage":["https://openalex.org/P4310319982"],"host_organization_lineage_names":["Science Press"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Computer Research and Development","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7135922198","https://openalex.org/W3145021318","https://openalex.org/W7101240020","https://openalex.org/W2970784940","https://openalex.org/W1904950888","https://openalex.org/W2117212296","https://openalex.org/W2121615264","https://openalex.org/W7095958055","https://openalex.org/W2019903958","https://openalex.org/W2354047161"],"abstract_inverted_index":{"The":[0],"fundamental":[1],"notion":[2],"of":[3],"active":[4,17,35,85],"learning":[5,36,86],"has":[6],"a":[7,95],"long":[8],"history":[9],"in":[10,94],"machine":[11],"learning.":[12],"In":[13],"this":[14],"paper,":[15],"an":[16],"Bayesian":[18],"net":[19],"model":[20,75],"is":[21,40,52,66],"provided":[22],"and":[23,46,49,59,73,79,92],"several":[24],"common":[25],"methods":[26,87],"for":[27,34],"sampling":[28,58],"are":[29,37],"given.":[30],"Then":[31],"two":[32],"strategies":[33],"discussed:":[38],"one":[39],"the":[41,50,53,56,67,71,84,99],"method":[42,54,68],"based":[43],"on":[44],"maximizing":[45],"minimizing":[47,60],"entropy,":[48],"other":[51],"combining":[55],"uncertainty":[57],"classification":[61],"loss.":[62],"Meanwhile,":[63],"also":[64],"given":[65],"that":[69,83],"classifies":[70],"example":[72],"update":[74],"parameters":[76],"incrementally.":[77],"Artificial":[78],"practical":[80],"experiments":[81],"show":[82],"proposed":[88],"have":[89],"high":[90],"precision":[91],"recalls":[93],"few":[96],"examples":[97],"with":[98],"class":[100],"label.":[101]},"counts_by_year":[{"year":2014,"cited_by_count":1}],"updated_date":"2026-10-03T13:16:28.481748","created_date":"2016-06-24T00:00:00"}
