{"id":"https://openalex.org/W2605300238","doi":"https://doi.org/10.3233/978-1-61499-722-1-248","title":"Exploring the Non-Trivial Knowledge Implicit in Test Instance to Fully Represent Unrestricted Bayesian Classifier","display_name":"Exploring the Non-Trivial Knowledge Implicit in Test Instance to Fully Represent Unrestricted Bayesian Classifier","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2605300238","doi":"https://doi.org/10.3233/978-1-61499-722-1-248","mag":"2605300238"},"language":"en","primary_location":{"id":"doi:10.3233/978-1-61499-722-1-248","is_oa":false,"landing_page_url":"https://doi.org/10.3233/978-1-61499-722-1-248","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","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/A5112269040","display_name":"Li Mei-Hui","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li Mei-Hui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100674511","display_name":"Limin Wang","orcid":"https://orcid.org/0000-0001-8640-3756"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang Li-Min","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112269040"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26363771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"248","last_page":"253"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.8774999976158142,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.8774999976158142,"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":0.7626000046730042,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.7526000142097473,"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":0.5829921364784241},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5589454174041748},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5192280411720276},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4703274667263031},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.46596625447273254},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33263489603996277}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5829921364784241},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5589454174041748},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5192280411720276},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4703274667263031},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46596625447273254},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33263489603996277}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3233/978-1-61499-722-1-248","is_oa":false,"landing_page_url":"https://doi.org/10.3233/978-1-61499-722-1-248","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},{"id":"mag:2605300238","is_oa":false,"landing_page_url":"http://dblp.uni-trier.de/db/conf/fsdm/fsdm2016.html#LiW16a","pdf_url":null,"source":{"id":"https://openalex.org/S4306510962","display_name":"FSDM","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"FSDM","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Restricted":[0],"Bayesian":[1,109],"classifiers":[2],"have":[3],"demonstrated":[4],"remarkable":[5],"classification":[6],"performance":[7],"for":[8,98],"data":[9],"mining.":[10],"However,":[11],"the":[12,21,30,34,41,61,65,69,82,93,107],"restricted":[13],"network":[14],"structure":[15,116],"makes":[16],"it":[17],"impossible":[18],"to":[19,29,75],"represent":[20,81],"Markov":[22,62],"blanket":[23,63],"of":[24,64],"class":[25,66],"variable,":[26],"which":[27,79],"corresponds":[28],"optimal":[31],"classifier.":[32],"And":[33],"test":[35,86],"instances":[36],"are":[37,90],"not":[38],"fully":[39],"utilized,":[40],"final":[42],"decision":[43],"thus":[44],"may":[45],"be":[46],"biased.":[47],"In":[48],"this":[49],"paper,":[50],"a":[51],"novel":[52],"unrestricted":[53,108],"k-dependence":[54],"classifier":[55,110],"is":[56],"proposed":[57],"based":[58],"on":[59],"identifying":[60],"variable.":[67],"Furthermore,":[68],"algorithm":[70],"also":[71],"adopts":[72],"local":[73,77],"learning":[74,96],"build":[76],"structure,":[78],"can":[80,111],"evidence":[83],"introduced":[84],"by":[85],"instance.":[87],"15":[88],"datasets":[89],"selected":[91],"from":[92],"UCI":[94],"machine":[95],"repository":[97],"zero-one":[99],"loss":[100],"comparison.":[101],"The":[102],"experimental":[103],"results":[104],"indicate":[105],"that":[106],"achieve":[112],"good":[113],"trade-off":[114],"between":[115],"complexity":[117],"and":[118],"prediction":[119],"performance.":[120]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
