{"id":"https://openalex.org/W4412526085","doi":"https://doi.org/10.3390/make7030070","title":"An Efficient and Accurate Random Forest Node-Splitting Algorithm Based on Dynamic Bayesian Methods","display_name":"An Efficient and Accurate Random Forest Node-Splitting Algorithm Based on Dynamic Bayesian Methods","publication_year":2025,"publication_date":"2025-07-21","ids":{"openalex":"https://openalex.org/W4412526085","doi":"https://doi.org/10.3390/make7030070"},"language":"en","primary_location":{"id":"doi:10.3390/make7030070","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030070","pdf_url":"https://www.mdpi.com/2504-4990/7/3/70/pdf?version=1753101637","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/3/70/pdf?version=1753101637","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111057559","display_name":"Jun He","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun He","raw_affiliation_strings":["School of Physics, Central South University, Changsha 410083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Physics, Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045193586","display_name":"Zhanqi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanqi Li","raw_affiliation_strings":["School of Electronic Information, Central South University, Changsha 410075, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information, Central South University, Changsha 410075, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100601134","display_name":"Linzi Yin","orcid":"https://orcid.org/0000-0001-8137-1273"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Linzi Yin","raw_affiliation_strings":["School of Electronic Information, Central South University, Changsha 410075, China"],"raw_orcid":"https://orcid.org/0000-0001-8137-1273","affiliations":[{"raw_affiliation_string":"School of Electronic Information, Central South University, Changsha 410075, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100601134"],"corresponding_institution_ids":["https://openalex.org/I139660479"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":2.0087,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.87499505,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"7","issue":"3","first_page":"70","last_page":"70"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.8517000079154968,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10057","display_name":"Face and Expression Recognition","score":0.8517000079154968,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7910000085830688,"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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.7631000280380249,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/random-forest","display_name":"Random forest","score":0.6356342434883118},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5831929445266724},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5735962986946106},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5478980541229248},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5260669589042664},{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.4965537190437317},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33012503385543823},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2624639570713043},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2538827061653137},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08200353384017944}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6356342434883118},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5831929445266724},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5735962986946106},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5478980541229248},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5260669589042664},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.4965537190437317},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33012503385543823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2624639570713043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2538827061653137},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08200353384017944},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7030070","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030070","pdf_url":"https://www.mdpi.com/2504-4990/7/3/70/pdf?version=1753101637","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1c22e4cedeb445318c00bfc374ee91f1","is_oa":true,"landing_page_url":"https://doaj.org/article/1c22e4cedeb445318c00bfc374ee91f1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 7, Iss 3, p 70 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7030070","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030070","pdf_url":"https://www.mdpi.com/2504-4990/7/3/70/pdf?version=1753101637","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1207471103","display_name":null,"funder_award_id":"52307232","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5408037771","display_name":null,"funder_award_id":"2024JJ4055","funder_id":"https://openalex.org/F4320322843","funder_display_name":"Natural Science Foundation of\u00a0Hunan Province"},{"id":"https://openalex.org/G5536145248","display_name":null,"funder_award_id":"2024JJ4055","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412526085.pdf","grobid_xml":"https://content.openalex.org/works/W4412526085.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W2068714596","https://openalex.org/W2129000925","https://openalex.org/W2149721706","https://openalex.org/W2472100207","https://openalex.org/W2517850251","https://openalex.org/W2736577301","https://openalex.org/W2765889385","https://openalex.org/W2778314063","https://openalex.org/W2790004940","https://openalex.org/W2892485383","https://openalex.org/W2910992412","https://openalex.org/W2911964244","https://openalex.org/W2914419238","https://openalex.org/W2953914835","https://openalex.org/W2962774976","https://openalex.org/W2965813329","https://openalex.org/W3002095816","https://openalex.org/W3094330920","https://openalex.org/W3114526902","https://openalex.org/W3159623433","https://openalex.org/W3172837678","https://openalex.org/W4200065055","https://openalex.org/W4206547457","https://openalex.org/W4210814507","https://openalex.org/W4282042832","https://openalex.org/W4285082157","https://openalex.org/W4292382515","https://openalex.org/W4297957988","https://openalex.org/W4311723097","https://openalex.org/W4377030089","https://openalex.org/W4377042322","https://openalex.org/W4386648688","https://openalex.org/W4392096137","https://openalex.org/W6749067469"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W1924178503","https://openalex.org/W3135126032","https://openalex.org/W4308716060","https://openalex.org/W4280648719","https://openalex.org/W4386937079","https://openalex.org/W1964038743"],"abstract_inverted_index":{"Random":[0,22],"Forests":[1,23],"are":[2],"powerful":[3],"machine":[4],"learning":[5],"models":[6],"widely":[7],"applied":[8],"in":[9,31,93,130,180],"classification":[10,152],"and":[11,74,136,154],"regression":[12,156],"tasks":[13,153],"due":[14],"to":[15,48,115,126,134,143,172],"their":[16],"robust":[17],"predictive":[18],"performance.":[19,141],"Nevertheless,":[20],"traditional":[21],"face":[24],"computational":[25],"challenges":[26],"during":[27],"tree":[28,51],"construction,":[29],"particularly":[30,176],"high-dimensional":[32],"data":[33],"or":[34,138],"on":[35,89,151],"resource-constrained":[36],"devices.":[37],"In":[38],"this":[39],"paper,":[40],"a":[41,54,64,127],"novel":[42],"node-splitting":[43],"algorithm,":[44],"BayesSplit,":[45],"is":[46],"proposed":[47],"accelerate":[49],"decision":[50],"construction":[52],"via":[53],"Bayesian-based":[55],"impurity":[56,61,91],"estimation":[57],"framework.":[58],"BayesSplit":[59,113,147,165],"treats":[60],"reduction":[62,129],"as":[63],"Bernoulli":[65],"event":[66],"with":[67,118,161],"Beta-conjugate":[68],"priors":[69],"for":[70,178],"each":[71],"split":[72],"point":[73],"incorporates":[75],"two":[76],"main":[77],"strategies.":[78],"First,":[79],"Dynamic":[80],"Posterior":[81],"Parameter":[82],"Refinement":[83],"updates":[84],"the":[85,144],"Beta":[86],"parameters":[87],"based":[88],"observed":[90],"reductions":[92],"batch":[94],"iterations.":[95],"Second,":[96],"Posterior-Derived":[97],"Confidence":[98],"Bounding":[99],"establishes":[100],"statistical":[101],"confidence":[102],"intervals,":[103],"efficiently":[104],"filtering":[105],"out":[106],"suboptimal":[107],"splits.":[108],"Theoretical":[109],"analysis":[110],"demonstrates":[111],"that":[112],"converges":[114],"optimal":[116],"splits":[117],"high":[119],"probability,":[120],"while":[121],"experimental":[122],"results":[123],"show":[124],"up":[125,171],"95%":[128],"training":[131,157],"time":[132,158],"compared":[133],"baselines":[135],"maintains":[137],"exceeds":[139],"generalization":[140],"Compared":[142],"state-of-the-art":[145],"MABSplit,":[146],"achieves":[148],"similar":[149],"accuracy":[150],"reduces":[155],"by":[159,170],"20\u201370%":[160],"lower":[162],"MSEs.":[163],"Furthermore,":[164],"enhances":[166],"feature":[167],"importance":[168],"stability":[169],"40%,":[173],"making":[174],"it":[175],"suitable":[177],"deployment":[179],"computationally":[181],"constrained":[182],"environments.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}