{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T13:48:23Z","timestamp":1782308903378,"version":"3.54.5"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013061","name":"Jilin Provincial Scientific and Technological Development Program","doi-asserted-by":"publisher","award":["20250205059GH"],"award-info":[{"award-number":["20250205059GH"]}],"id":[{"id":"10.13039\/501100013061","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014760","name":"Ministry of Ecology and Environment, The People\u2019s Republic of China","doi-asserted-by":"publisher","award":["NKPMF202403"],"award-info":[{"award-number":["NKPMF202403"]}],"id":[{"id":"10.13039\/501100014760","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115111","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:32:05Z","timestamp":1781872325000},"page":"115111","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["From log-likelihood to probability-likelihood: A novel approach to enhancing classification performance by in-depth Bayesian inference"],"prefix":"10.1016","volume":"181","author":[{"given":"Xinyu","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7742-669X","authenticated-orcid":false,"given":"Limin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaowa","family":"Nayin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taosheng","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115111_b1","doi-asserted-by":"crossref","unstructured":"Akaike, H., A new look at the statistical model identification. IEEE Trans. Autom. Control 19 (6), 716\u2013723.","DOI":"10.1109\/TAC.1974.1100705"},{"issue":"7","key":"10.1016\/j.engappai.2026.115111_b2","article-title":"Discriminative learning of bayesian networks via factorized conditional log-likelihood","volume":"12","author":"Carvalho","year":"2011","journal-title":"J. Mach. Learn. Res."},{"issue":"9","key":"10.1016\/j.engappai.2026.115111_b3","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1109\/TSE.2021.3101870","article-title":"Enhancing dynamic symbolic execution by automatically learning search heuristics","volume":"48","author":"Cha","year":"2021","journal-title":"IEEE Trans. Softw. Eng."},{"key":"10.1016\/j.engappai.2026.115111_b4","first-page":"121","article-title":"Learning bayesian networks is np-complete. learning from data","author":"Chickering","year":"1996","journal-title":"Artif. intell. stat. V"},{"key":"10.1016\/j.engappai.2026.115111_b5","series-title":"Pattern classification","author":"Du","year":"2012"},{"key":"10.1016\/j.engappai.2026.115111_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106085","article-title":"Instance-based weighting filter for superparent one-dependence estimators","volume":"203","author":"Duan","year":"2020","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"10.1016\/j.engappai.2026.115111_b7","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TIM.2004.838132","article-title":"Modified aic and mdl model selection criteria for short data records","volume":"54","author":"F. De Ridder","year":"2005","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115111_b8","unstructured":"Fayyad, U.M., Irani, K.B., 1993. Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence. pp. 1022\u20131029."},{"issue":"3","key":"10.1016\/j.engappai.2026.115111_b9","first-page":"37","article-title":"From data mining to knowledge discovery in databases","volume":"17","author":"Fayyad","year":"1996","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.engappai.2026.115111_b10","doi-asserted-by":"crossref","unstructured":"Flores, M.J., J.A. G\u00e1mez, J.M. Puerta (2009, June) GAODE and HAODE: two proposals based on AODE to deal with continuous variables. In: Proceedings of the 26th Annual International Conference on Machine Learning. pp. 313\u2013320.","DOI":"10.1145\/1553374.1553414"},{"key":"10.1016\/j.engappai.2026.115111_b11","doi-asserted-by":"crossref","unstructured":"Friedman, N., Geiger, D., Goldszmidt, M., Bayesian network classifiers. Mach. Learn. 29, 131\u2013163.","DOI":"10.1023\/A:1007465528199"},{"issue":"4","key":"10.1016\/j.engappai.2026.115111_b12","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/TBDATA.2023.3338019","article-title":"Learning balanced bayesian classifiers from labeled and unlabeled data","volume":"10","author":"Guo","year":"2024","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.engappai.2026.115111_b13","doi-asserted-by":"crossref","unstructured":"Hainmueller, J., Entropy balancing for causal effects: A multivari- ate reweighting method to produce balanced samples in observational studies. Political Anal. 20 (1), 25\u201346.","DOI":"10.1093\/pan\/mpr025"},{"key":"10.1016\/j.engappai.2026.115111_b14","doi-asserted-by":"crossref","unstructured":"J. D. Rodr\u00ed guez, J. A. Lozano, 2008. Multi-objective learning of multi-dimensional Bayesian classifiers. In: 2008 Eighth International Conference on Hybrid Intelligent Systems. pp. 501\u2013506.","DOI":"10.1109\/HIS.2008.143"},{"key":"10.1016\/j.engappai.2026.115111_b15","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.knosys.2011.08.010","article-title":"Improving tree augmented naive bayes for class probability estimation","volume":"26","author":"Jiang","year":"2012","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"10.1016\/j.engappai.2026.115111_b16","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1080\/0952813X.2011.639092","article-title":"Weighted average of one-dependence estimators","volume":"24","author":"Jiang","year":"2012","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115111_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109473","article-title":"Flexible model weighting for one-dependence estimators based on point-wise independence analysis","volume":"139","author":"Kong","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.115111_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106422","article-title":"Learning semi-lazy bayesian network classifier under the ciid assumption","volume":"208","author":"Liu","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.115111_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106627","article-title":"Hierarchical independence thresholding for learning bayesian network classifiers","volume":"212","author":"Liu","year":"2021","journal-title":"Knowl.-Based Syst."},{"issue":"14","key":"10.1016\/j.engappai.2026.115111_b20","doi-asserted-by":"crossref","first-page":"11583","DOI":"10.1016\/j.eswa.2012.04.024","article-title":"Bagging k-dependence probabilistic networks: an alternative powerful fraud detection tool","volume":"39","author":"Louzada","year":"2012","journal-title":"Expert. Syst. Appl."},{"issue":"6","key":"10.1016\/j.engappai.2026.115111_b21","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/TSP.2012.2234747","article-title":"Flexible detection criterion for source enumeration in array processing","volume":"61","author":"Lu","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"issue":"1","key":"10.1016\/j.engappai.2026.115111_b22","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1111\/rssb.12023","article-title":"Model selection principles in misspecified models","volume":"76","author":"Lv","year":"2014","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"10.1016\/j.engappai.2026.115111_b23","unstructured":"Ma, S.C., Shi, H.B., 2004. Tree-augmented naive Bayes ensembles. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics. pp. 1497\u20131502."},{"key":"10.1016\/j.engappai.2026.115111_b24","doi-asserted-by":"crossref","unstructured":"Pang, M., Wang, L., Li, Q., Guo, L., Li, K., (2023) Learning bayesian multinets from labeled and unlabeled data for knowledge representation. Intell. Data Anal. 27 (6), 1699\u20131723.","DOI":"10.3233\/IDA-227068"},{"key":"10.1016\/j.engappai.2026.115111_b25","doi-asserted-by":"crossref","first-page":"15496","DOI":"10.1007\/s10489-022-03356-z","article-title":"Stochastic optimization for bayesian network classifiers","volume":"52","author":"Ren","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.engappai.2026.115111_b26","first-page":"461","article-title":"Estimating the dimension of a model","author":"Schwarz","year":"1978","journal-title":"Ann. Statist."},{"key":"10.1016\/j.engappai.2026.115111_b27","doi-asserted-by":"crossref","unstructured":"Seghouane, A.K., Model selection criteria for image restoration. IEEE Trans. Neural Netw. 20 (8), 1357\u20131363.","DOI":"10.1109\/TNN.2009.2024146"},{"key":"10.1016\/j.engappai.2026.115111_b28","series-title":"Knowledge Discovery and Data Mining","first-page":"26","article-title":"Clustering using monte carlo cross-validation","author":"Smyth","year":"1996"},{"issue":"1","key":"10.1016\/j.engappai.2026.115111_b29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1080\/03610918.2014.955112","article-title":"Interpreting out-of-control signals using instance-based bayesian classifier in multivariate statistical process control","volume":"46","author":"Song","year":"2017","journal-title":"Comm. Statist. Simulation Comput."},{"key":"10.1016\/j.engappai.2026.115111_b30","doi-asserted-by":"crossref","first-page":"122395","DOI":"10.1016\/j.eswa.2023.122395","article-title":"Learning high-dependence bayesian network classifier with robust topology","volume":"239","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.115111_b31","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.3233\/IDA-226806","article-title":"Exploiting the implicit independence assumption for learning directed graphical models","volume":"27","author":"Wang","year":"2023","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.engappai.2026.115111_b32","doi-asserted-by":"crossref","first-page":"109078","DOI":"10.1016\/j.knosys.2022.109078","article-title":"Alleviating the attribute conditional independence and i. i. d. assumptions of averaged one-dependence estimator by double weighting","volume":"250","author":"Wang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.115111_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105212","article-title":"Learning causal bayesian networks based on causality analysis for classification","volume":"114","author":"Wang","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115111_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105212","article-title":"Learning causal bayesian networks based on causality analysis for classification","volume":"114","author":"Wang","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115111_b35","doi-asserted-by":"crossref","unstructured":"Webb, G.I., Boughton, J.R., Wang, Z., Not so naive Bayes: aggregating one-dependence estimators. Mach. Learn. 58, 5\u201324.","DOI":"10.1007\/s10994-005-4258-6"},{"key":"10.1016\/j.engappai.2026.115111_b36","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ins.2022.03.037","article-title":"Graph-based sparse bayesian broad learning system for semi-supervised learning","volume":"597","author":"Xu","year":"2022","journal-title":"Inform. Sci."},{"issue":"22","key":"10.1016\/j.engappai.2026.115111_b37","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.3390\/math9222982","article-title":"A novel hybrid approach: instance weighted hidden naive bayes","volume":"9","author":"Yu","year":"2021","journal-title":"Mathematics"},{"key":"10.1016\/j.engappai.2026.115111_b38","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s10115-024-02238-9","article-title":"Probability knowledge acquisition from unlabeled instance based on dual learning","volume":"67","author":"Zhao","year":"2024","journal-title":"Knowl. Inf. Syst."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626013941?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626013941?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T13:08:30Z","timestamp":1782306510000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626013941"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":38,"alternative-id":["S0952197626013941"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115111","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"From log-likelihood to probability-likelihood: A novel approach to enhancing classification performance by in-depth Bayesian inference","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115111","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115111"}}