{"id":"https://openalex.org/W7154897769","doi":"https://doi.org/10.1016/j.asoc.2026.115284","title":"Computing conditional probabilities in Bayesian networks using logistic regression","display_name":"Computing conditional probabilities in Bayesian networks using logistic regression","publication_year":2026,"publication_date":"2026-04-19","ids":{"openalex":"https://openalex.org/W7154897769","doi":"https://doi.org/10.1016/j.asoc.2026.115284"},"language":"en","primary_location":{"id":"doi:10.1016/j.asoc.2026.115284","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.115284","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.asoc.2026.115284","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008393074","display_name":"Seraf\u0131\u0301n Moral","orcid":"https://orcid.org/0000-0002-5555-0857"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Seraf\u00edn Moral","raw_affiliation_strings":["Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064232735","display_name":"Seraf\u00edn Moral\u2010Garc\u00eda","orcid":"https://orcid.org/0000-0002-8513-9081"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Seraf\u00edn Moral-Garc\u00eda","raw_affiliation_strings":["Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002770026","display_name":"Andr\u00e9s Cano","orcid":"https://orcid.org/0000-0001-7650-1221"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Andr\u00e9s Cano","raw_affiliation_strings":["Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain"],"raw_orcid":"https://orcid.org/0000-0001-7650-1221","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057510662","display_name":"Manuel G\u00f3mez\u2010Olmedo","orcid":"https://orcid.org/0000-0002-3817-8723"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Manuel G\u00f3mez-Olmedo","raw_affiliation_strings":["Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain"],"raw_orcid":"https://orcid.org/0000-0002-3817-8723","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5064232735"],"corresponding_institution_ids":["https://openalex.org/I173304897"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":{"value":3350,"currency":"USD","value_usd":3350},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.47566909,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"198","issue":null,"first_page":"115284","last_page":"115284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.8744999766349792,"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":0.8744999766349792,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.013299999758601189,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.009800000116229057,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6345999836921692},{"id":"https://openalex.org/keywords/logistic-model-tree","display_name":"Logistic model tree","score":0.5128999948501587},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4260999858379364},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4255000054836273},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4237000048160553},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4018999934196472},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.39879998564720154},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.35910001397132874},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.3467999994754791}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6345999836921692},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.5128999948501587},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49300000071525574},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45649999380111694},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4528999924659729},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4260999858379364},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4237000048160553},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40790000557899475},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3693000078201294},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.35910001397132874},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.3467999994754791},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2896000146865845},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.28220000863075256},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.28139999508857727},{"id":"https://openalex.org/C27574286","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Variables","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.2662000060081482},{"id":"https://openalex.org/C203868755","wikidata":"https://www.wikidata.org/wiki/Q5353562","display_name":"Elastic net regularization","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C93698799","wikidata":"https://www.wikidata.org/wiki/Q5428730","display_name":"Factor regression model","level":5,"score":0.2522999942302704},{"id":"https://openalex.org/C57381214","wikidata":"https://www.wikidata.org/wiki/Q55631393","display_name":"Regression diagnostic","level":4,"score":0.2515999972820282},{"id":"https://openalex.org/C94465730","wikidata":"https://www.wikidata.org/wiki/Q589603","display_name":"Logistic distribution","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.asoc.2026.115284","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.115284","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"},{"id":"pmh:oai:digibug.ugr.es:10481/113367","is_oa":true,"landing_page_url":"https://hdl.handle.net/10481/113367","pdf_url":"https://digibug.ugr.es/bitstream/10481/113367/1/1-s2.0-S1568494626007325-main.pdf","source":{"id":"https://openalex.org/S4306400567","display_name":"Institutional Repository of the University of Granada (University of Granada)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I173304897","host_organization_name":"Universidad de Granada","host_organization_lineage":["https://openalex.org/I173304897"],"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":null,"raw_type":"journal article"}],"best_oa_location":{"id":"doi:10.1016/j.asoc.2026.115284","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.asoc.2026.115284","pdf_url":null,"source":{"id":"https://openalex.org/S140556538","display_name":"Applied Soft Computing","issn_l":"1568-4946","issn":["1568-4946","1872-9681"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Soft Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7074459791183472}],"awards":[],"funders":[{"id":"https://openalex.org/F4320315062","display_name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","ror":null},{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2049633694","https://openalex.org/W2072063551","https://openalex.org/W2079796986","https://openalex.org/W2110886576","https://openalex.org/W2149706766","https://openalex.org/W2152511329","https://openalex.org/W2162586165","https://openalex.org/W2170112109","https://openalex.org/W2275580678","https://openalex.org/W2611159092","https://openalex.org/W2977689220","https://openalex.org/W4410899189","https://openalex.org/W4415797473"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"addresses":[2],"the":[3,17,25,69,97,101],"estimation":[4],"of":[5,19,27,82,96],"conditional":[6,160],"probability":[7,139,161],"tables":[8],"in":[9],"Bayesian":[10,135],"networks.":[11],"Traditional":[12],"procedures":[13,137],"encounter":[14],"difficulties":[15],"when":[16],"number":[18,26],"parent":[20],"variables":[21,66],"is":[22,103,114],"large":[23],"because":[24],"parameters":[28,156],"grows":[29],"exponentially.":[30],"Logistic":[31],"regression":[32,130,147],"can":[33],"mitigate":[34],"this":[35],"dimensionality":[36],"challenge,":[37],"but":[38],"it":[39],"imposes":[40],"assumptions":[41],"about":[42],"how":[43],"a":[44,60,74,158],"variable":[45],"depends":[46],"on":[47,93,105,121],"its":[48,52],"parents.":[49],"To":[50],"extend":[51],"applicability":[53],"to":[54,78],"more":[55],"general":[56,75],"scenarios,":[57],"we":[58,127],"introduce":[59],"method":[61],"for":[62],"systematically":[63],"constructing":[64],"artificial":[65],"derived":[67],"from":[68],"original":[70,98],"predictors.":[71],"We":[72],"develop":[73],"theoretical":[76],"framework":[77],"compare":[79],"alternative":[80],"formulations":[81],"these":[83],"variables.":[84],"Two":[85],"main":[86],"approaches":[87],"are":[88],"proposed.":[89],"The":[90,111,142],"first":[91],"relies":[92],"XOR":[94],"combinations":[95],"variables,":[99],"whereas":[100],"second":[102],"based":[104],"Linear":[106],"Discriminant":[107],"Analysis":[108],"and":[109,123,138,148],"discretization.":[110],"proposed":[112],"approach":[113],"validated":[115],"through":[116],"an":[117],"extensive":[118],"empirical":[119],"evaluation":[120],"large-":[122],"medium-scale":[124],"networks,":[125],"where":[126],"benchmark":[128],"logistic":[129,146],"against":[131],"established":[132],"techniques,":[133],"including":[134],"inference":[136],"tree":[140],"models.":[141],"experiments":[143],"demonstrate":[144],"that":[145],"discretization":[149],"yield":[150],"excellent":[151],"performance":[152],"while":[153],"requiring":[154],"fewer":[155],"than":[157],"full":[159],"table.":[162]},"counts_by_year":[],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2026-04-20T00:00:00"}
