{"id":"https://openalex.org/W3082885752","doi":"https://doi.org/10.1109/icaica50127.2020.9182465","title":"A Node Sorting Method for K2 Algorithm in Bayesian Network Structure Learning","display_name":"A Node Sorting Method for K2 Algorithm in Bayesian Network Structure Learning","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3082885752","doi":"https://doi.org/10.1109/icaica50127.2020.9182465","mag":"3082885752"},"language":"en","primary_location":{"id":"doi:10.1109/icaica50127.2020.9182465","doi":"https://doi.org/10.1109/icaica50127.2020.9182465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaica50127.2020.9182465","pdf_url":null,"source":{"id":"https://openalex.org/S4363608013","display_name":"IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5036632140","display_name":"Fan Gao","orcid":"https://orcid.org/0000-0001-9207-7635","observed_orcids":["https://orcid.org/0000-0001-9207-7635","https://orcid.org/0009-0004-7502-2287"]},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Gao","raw_affiliation_strings":["The State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023513469","display_name":"Da Huang","orcid":"https://orcid.org/0000-0002-4470-0979","observed_orcids":["https://orcid.org/0000-0002-4470-0979"]},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Da Huang","raw_affiliation_strings":["The State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.1592,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.36309003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"106","last_page":"110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","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/T11303","display_name":"Bayesian Modeling and Causal Inference","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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-network-structure-learning","display_name":"Bayesian network structure learning","score":0.9940000176429749},{"id":"https://openalex.org/keywords/k2-algorithm","display_name":"K2 algorithm","score":0.9959999918937683},{"id":"https://openalex.org/keywords/node-ranking","display_name":"node ranking","score":0.7910000085830688},{"id":"https://openalex.org/keywords/node-priority","display_name":"node priority","score":0.9409999847412109},{"id":"https://openalex.org/keywords/mcmc-sampling","display_name":"MCMC sampling","score":0.7900000214576721},{"id":"https://openalex.org/keywords/weighted-scoring","display_name":"weighted scoring","score":0.8379999995231628}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7409206628799438},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.6988991498947144},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6683545112609863},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.627903938293457},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5185925960540771},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5147509574890137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49860405921936035},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.45832741260528564},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4271967113018036},{"id":"https://openalex.org/C108094655","wikidata":"https://www.wikidata.org/wiki/Q181593","display_name":"Sorting algorithm","level":3,"score":0.423110693693161},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.4198967218399048},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37048494815826416},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.35648077726364136},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.21929290890693665},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.147761732339859},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaica50127.2020.9182465","doi":"https://doi.org/10.1109/icaica50127.2020.9182465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaica50127.2020.9182465","pdf_url":null,"source":{"id":"https://openalex.org/S4363608013","display_name":"IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W70873205","https://openalex.org/W1587150650","https://openalex.org/W1991272308","https://openalex.org/W2087635140","https://openalex.org/W2159985326","https://openalex.org/W2165190832","https://openalex.org/W2797721853","https://openalex.org/W2830852141","https://openalex.org/W2986428434","https://openalex.org/W2997606968","https://openalex.org/W4285719527","https://openalex.org/W6635147717"],"related_works":["https://openalex.org/W7104371444","https://openalex.org/W4226054837","https://openalex.org/W2206398749","https://openalex.org/W2830852141","https://openalex.org/W3034523642","https://openalex.org/W2087635140","https://openalex.org/W3015121100","https://openalex.org/W2998055684","https://openalex.org/W2159985326","https://openalex.org/W1515064056"],"abstract_inverted_index":{"Bayesian":[0,23,70],"network":[1,24,71,90],"is":[2,16,64,94],"an":[3,9,126],"important":[4],"model":[5],"for":[6,34],"reasoning":[7],"in":[8,88],"uncertain":[10],"environment.":[11],"A":[12],"reliable":[13],"node":[14,31,40,56,87],"rank":[15,32,57,93],"required":[17],"by":[18],"K2":[19,35],"algorithm":[20,49,63,111],"to":[21,53,67],"learn":[22,54],"structure":[25],"better.":[26],"To":[27],"provide":[28],"a":[29,39,55],"high-quality":[30],"tailored":[33],"algorithm,":[36,124],"we":[37],"propose":[38],"priority-based":[41],"sorting":[42,110,121],"algorithm.":[43],"Given":[44],"observable":[45],"data":[46],"only,":[47],"our":[48,109],"can":[50,74],"be":[51],"employed":[52],"without":[58],"expert":[59],"knowledge.":[60],"Specifically,":[61],"MCMC":[62,123],"first":[65],"utilized":[66],"yield":[68],"some":[69],"structures":[72],"that":[73,108],"sufficiently":[75],"fit":[76],"the":[77,83,102],"observed":[78],"data.":[79],"We":[80],"then":[81],"define":[82],"priority":[84],"of":[85],"each":[86],"these":[89],"structures.":[91],"Node":[92],"finally":[95],"obtained":[96],"through":[97],"weighted":[98],"scoring":[99],"based":[100],"on":[101,125],"priority.":[103],"The":[104],"empirical":[105],"results":[106],"show":[107],"performs":[112],"significantly":[113],"better":[114],"than":[115],"commonly":[116],"used":[117],"methods,":[118],"e.g.,":[119],"randomly":[120],"and":[122],"Asia":[127],"network-learning":[128],"dataset.":[129]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-10-05T09:50:58.205359","created_date":"2020-09-08T00:00:00"}