{"id":"https://openalex.org/W4417216478","doi":"https://doi.org/10.3389/fdata.2025.1706417","title":"Posterior averaging with Gaussian naive Bayes and the R package RandomGaussianNB for big-data classification","display_name":"Posterior averaging with Gaussian naive Bayes and the R package RandomGaussianNB for big-data classification","publication_year":2025,"publication_date":"2025-12-11","ids":{"openalex":"https://openalex.org/W4417216478","doi":"https://doi.org/10.3389/fdata.2025.1706417","pmid":"https://pubmed.ncbi.nlm.nih.gov/41458061"},"language":"en","primary_location":{"id":"doi:10.3389/fdata.2025.1706417","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2025.1706417","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1706417/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1706417/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117795226","display_name":"Patchanok Srisuradetchai","orcid":null},"institutions":[{"id":"https://openalex.org/I108108428","display_name":"Thammasat University","ror":"https://ror.org/002yp7f20","country_code":"TH","type":"education","lineage":["https://openalex.org/I108108428"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Patchanok Srisuradetchai","raw_affiliation_strings":["Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Khlong Luang, Pathum Thani, Thailand"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Khlong Luang, Pathum Thani, Thailand","institution_ids":["https://openalex.org/I108108428"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5117795226"],"corresponding_institution_ids":["https://openalex.org/I108108428"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":2.7163,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93012904,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1706417","last_page":"1706417"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.11630000174045563,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.11630000174045563,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.10740000009536743,"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":0.09830000251531601,"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/interpretability","display_name":"Interpretability","score":0.8651000261306763},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.6261000037193298},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5788999795913696},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5706999897956848},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5364999771118164},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.5235999822616577},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5134999752044678},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5126000046730042},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4893999993801117}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8651000261306763},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.6261000037193298},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5788999795913696},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5706999897956848},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.550000011920929},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5364999771118164},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5235999822616577},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5134999752044678},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5126000046730042},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4893999993801117},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4700999855995178},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4674000144004822},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.4542999863624573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4383000135421753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4083999991416931},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3935999870300293},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.384799987077713},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3531000018119812},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.32420000433921814},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.29589998722076416},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2605000138282776}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/fdata.2025.1706417","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2025.1706417","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1706417/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},{"id":"pmid:41458061","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41458061","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in big data","raw_type":null},{"id":"pmh:oai:doaj.org/article:11912c3ba7cb4a3e9e31c075953fb60b","is_oa":true,"landing_page_url":"https://doaj.org/article/11912c3ba7cb4a3e9e31c075953fb60b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Big Data, Vol 8 (2025)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:12738300","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12738300/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Big Data","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3389/fdata.2025.1706417","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2025.1706417","pdf_url":"https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1706417/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4417216478.pdf"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1507985183","https://openalex.org/W1510073064","https://openalex.org/W1534477342","https://openalex.org/W1590183771","https://openalex.org/W1817561967","https://openalex.org/W1831050183","https://openalex.org/W1988790447","https://openalex.org/W1995945562","https://openalex.org/W2005861721","https://openalex.org/W2032026767","https://openalex.org/W2064675550","https://openalex.org/W2073241381","https://openalex.org/W2076118331","https://openalex.org/W2112796928","https://openalex.org/W2113242816","https://openalex.org/W2119717791","https://openalex.org/W2140785063","https://openalex.org/W2152885278","https://openalex.org/W2167917621","https://openalex.org/W2168020168","https://openalex.org/W2216946510","https://openalex.org/W2318802957","https://openalex.org/W2789758093","https://openalex.org/W2793350103","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2920270798","https://openalex.org/W2964278775","https://openalex.org/W2981170770","https://openalex.org/W2997591727","https://openalex.org/W3018761215","https://openalex.org/W3205846040","https://openalex.org/W4232478844","https://openalex.org/W4289236186","https://openalex.org/W4320015075","https://openalex.org/W4383681686","https://openalex.org/W4402690135","https://openalex.org/W4409157509","https://openalex.org/W4409480980","https://openalex.org/W4410940659","https://openalex.org/W4411960662"],"related_works":[],"abstract_inverted_index":{"RandomGaussianNB":[0],"is":[1],"an":[2,141],"open-source":[3],"R":[4],"package":[5,85],"implementing":[6],"the":[7,19,47,50,79,129],"posterior-averaging":[8],"Gaussian":[9],"naive":[10,148],"Bayes":[11,149],"(PAV-GNB)":[12],"algorithm,":[13],"a":[14,60,75,87,125],"scalable":[15],"ensemble":[16,51,57,147],"extension":[17],"of":[18,49,78],"classical":[20],"GNB":[21],"classifier.":[22],"The":[23,84],"method":[24],"introduces":[25],"posterior":[26,66],"averaging":[27],"to":[28],"mitigate":[29],"correlation":[30],"bias":[31],"and":[32,41,59,103,110,124,137],"enhance":[33],"stability":[34],"in":[35,82],"high-dimensional":[36],"settings":[37],"while":[38],"maintaining":[39],"interpretability":[40],"computational":[42,138],"efficiency.":[43],"Theoretical":[44],"results":[45,73],"establish":[46],"variance":[48,67],"posterior,":[52],"which":[53],"decreases":[54],"inversely":[55],"with":[56,68,112,121],"size,":[58],"margin-based":[61],"generalization":[62],"bound":[63],"that":[64],"connects":[65],"classification":[69],"error.":[70],"Together,":[71],"these":[72],"provide":[74],"principled":[76],"understanding":[77],"bias-variance":[80],"trade-off":[81],"PAV-GNB.":[83],"delivers":[86],"fully":[88],"parallel,":[89],"reproducible":[90],"framework":[91],"for":[92,146],"large-scale":[93],"classification.":[94,150],"Simulation":[95],"studies":[96],"under":[97],"big-data":[98],"conditions-large":[99],"samples,":[100],"many":[101],"features,":[102],"multiple":[104],"classes-show":[105],"consistent":[106],"accuracy,":[107],"low":[108],"variance,":[109],"agreement":[111],"theoretical":[113],"predictions.":[114],"Scalability":[115],"experiments":[116],"demonstrate":[117],"near-linear":[118],"runtime":[119],"improvement":[120],"multi-core":[122],"execution,":[123],"real-world":[126],"application":[127],"on":[128],"Pima":[130],"Indians":[131],"Diabetes":[132],"dataset":[133],"validates":[134],"PAV-GNB's":[135],"reliability":[136],"efficiency":[139],"as":[140],"interpretable,":[142],"statistically":[143],"grounded":[144],"approach":[145]},"counts_by_year":[],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2025-12-11T00:00:00"}