{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:31:32Z","timestamp":1760059892988,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61573285","G2022KY0602","21RGZN0016"],"award-info":[{"award-number":["61573285","G2022KY0602","21RGZN0016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["61573285","G2022KY0602","21RGZN0016"],"award-info":[{"award-number":["61573285","G2022KY0602","21RGZN0016"]}]},{"name":"key core technology research plan of Xi\u2019an, China","award":["61573285","G2022KY0602","21RGZN0016"],"award-info":[{"award-number":["61573285","G2022KY0602","21RGZN0016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Bayesian networks (BNs) are effective and universal tools for addressing uncertain knowledge. BN learning includes structure learning and parameter learning, and structure learning is its core. The topology of a BN can be determined by expert domain knowledge or obtained through data analysis. However, when many variables exist in a BN, relying only on expert knowledge is difficult and infeasible. Therefore, the current research focus is to build a BN via data analysis. However, current data learning methods have certain limitations. In this work, we consider a combination of expert knowledge and data learning methods. In our algorithm, the hard constraints are derived from highly reliable expert knowledge, and some conditional independent information is mined by feature selection as a soft constraint. These structural constraints are reasonably integrated into an exponential Monte Carlo with counter (EMCQ) hyper-heuristic algorithm. A comprehensive experimental study demonstrates that our proposed method exhibits more robustness and accuracy compared to alternative algorithms.<\/jats:p>","DOI":"10.3390\/axioms14070538","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T11:25:17Z","timestamp":1752751517000},"page":"538","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Hyper-Heuristic Algorithm for Bayesian Network Structure Learning Based on Feature Selection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7949-4092","authenticated-orcid":false,"given":"Yinglong","family":"Dang","sequence":"first","affiliation":[{"name":"School of Electronic and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Xiaoguang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5713-8375","authenticated-orcid":false,"given":"Zidong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119381","DOI":"10.1016\/j.eswa.2022.119381","article-title":"Lung nodule detection algorithm based on rank correlation causal structure learning","volume":"216","author":"Yang","year":"2023","journal-title":"Expert Syst. 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