{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T10:26:16Z","timestamp":1787826376730,"version":"build-2784847793"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020AAA0106100"],"award-info":[{"award-number":["2020AAA0106100"]}]},{"name":"Natural Science Foundation of Anhui Province of China","award":["2108085QF270 and 2008085QF307"],"award-info":[{"award-number":["2108085QF270 and 2008085QF307"]}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"crossref","award":["DP200101210"],"award-info":[{"award-number":["DP200101210"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:p>\n            Learning partial Bayesian network (BN) structure is an interesting and challenging problem. In this challenge, it is computationally expensive to use global BN structure learning algorithms, while only one part of a BN structure is interesting, local BN structure learning algorithms are not a favourable solution either due to the issue of false edge orientation. To address the problem, this article first presents a detailed analysis of the false edge orientation issue with local BN structure learning algorithms and then proposes PSL, an efficient and accurate\n            <jats:underline>P<\/jats:underline>\n            artial BN\n            <jats:underline>S<\/jats:underline>\n            tructure\n            <jats:underline>L<\/jats:underline>\n            earning (PSL) algorithm. Specifically, PSL divides V-structures in a Markov blanket (MB) into two types: Type-C V-structures and Type-NC V-structures, then it starts from the given node of interest and recursively finds both types of V-structures in the MB of the current node until all edges in the partial BN structure are oriented. To further improve the efficiency of PSL, the PSL-FS algorithm is designed by incorporating\n            <jats:underline>F<\/jats:underline>\n            eature\n            <jats:underline>S<\/jats:underline>\n            election (FS) into PSL. Extensive experiments with six benchmark BNs validate the efficiency and accuracy of the proposed algorithms.\n          <\/jats:p>","DOI":"10.1145\/3508071","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T14:03:20Z","timestamp":1646921000000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["PSL: An Algorithm for Partial Bayesian Network Structure Learning"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4812-6676","authenticated-orcid":false,"given":"Zhaolong","family":"Ling","sequence":"first","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kui","family":"Yu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"University of South Australia, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"University of South Australia, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Mininglamp Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","first-page":"235","article-title":"Local causal and markov blanket induction for causal discovery and feature selection for classification part ii: Analysis and extensions","volume":"11","author":"Aliferis Constantin F.","year":"2010","unstructured":"Constantin F. 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