{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T01:03:55Z","timestamp":1787619835049,"version":"build-2736575974"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"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"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62376087, 62306002, 62376085"],"award-info":[{"award-number":["62376087, 62306002, 62376085"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,2,29]]},"abstract":"<jats:p>Local-to-global learning approach plays an essential role in Bayesian network (BN) structure learning. Existing local-to-global learning algorithms first construct the skeleton of a DAG (directed acyclic graph) by learning the MB (Markov blanket) or PC (parents and children) of each variable in a dataset, then orient edges in the skeleton. However, existing MB or PC learning methods are often computationally expensive especially with a large-sized BN, resulting in inefficient local-to-global learning algorithms. To tackle the problem, in this article, we link feature selection with local BN structure learning and develop an efficient local-to-global learning approach using filtering feature selection. Specifically, we first analyze the rationale of the well-known Minimum-Redundancy and Maximum-Relevance (MRMR) feature selection approach for learning a PC set of a variable. Based on the analysis, we propose an efficient F2SL (feature selection-based structure learning) approach to local-to-global BN structure learning. The F2SL approach first employs the MRMR approach to learn the skeleton of a DAG, then orients edges in the skeleton. Employing independence tests or score functions for orienting edges, we instantiate the F2SL approach into two new algorithms, F2SL-c (using independence tests) and F2SL-s (using score functions). Compared to the state-of-the-art local-to-global BN learning algorithms, the experiments validated that the proposed algorithms in this article are more efficient and provide competitive structure learning quality than the compared algorithms.<\/jats:p>","DOI":"10.1145\/3624479","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T12:27:45Z","timestamp":1695126465000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Feature Selection for Efficient Local-to-global Bayesian Network Structure Learning"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2442-4572","authenticated-orcid":false,"given":"Kui","family":"Yu","sequence":"first","affiliation":[{"name":"Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4812-6676","authenticated-orcid":false,"given":"Zhaolong","family":"Ling","sequence":"additional","affiliation":[{"name":"Anhui University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2843-5738","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"University of South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9142-448X","authenticated-orcid":false,"given":"Peipei","family":"Li","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3916-0822","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-1878","authenticated-orcid":false,"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"University of South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"171","article-title":"Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: Algorithms and empirical evaluation","volume":"11","author":"Aliferis Constantin F.","year":"2010","unstructured":"Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, and Xenofon D. Koutsoukos. 2010. Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: Algorithms and empirical evaluation. J. Mach. Learn. Res. 11, Jan. (2010), 171\u2013234.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_3_2","first-page":"21","volume-title":"AMIA Annual Symposium Proceedings","volume":"2003","author":"Aliferis Constantin F.","year":"2003","unstructured":"Constantin F. Aliferis, Ioannis Tsamardinos, and Alexander Statnikov. 2003. HITON: A novel Markov blanket algorithm for optimal variable selection. In AMIA Annual Symposium Proceedings, Vol. 2003. American Medical Informatics Association, 21."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2014.00131"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2188387"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(02)00191-1"},{"key":"e_1_3_2_7_2","first-page":"507","article-title":"Optimal structure identification with greedy search","volume":"3","author":"Chickering David Maxwell","year":"2002","unstructured":"David Maxwell Chickering. 2002. Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, Nov. (2002), 507\u2013554.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_8_2","first-page":"1287","article-title":"Large-sample learning of Bayesian networks is NP-hard","volume":"5","author":"Chickering David Maxwell","year":"2004","unstructured":"David Maxwell Chickering, David Heckerman, and Christopher Meek. 2004. Large-sample learning of Bayesian networks is NP-hard. J. Mach. Learn. Res. 5, Oct. (2004), 1287\u20131330.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2750365"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.04.002"},{"issue":"5","key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.ipm.2004.03.001","article-title":"Bayesian networks and information retrieval: An introduction to the special issue","volume":"40","author":"Campos Luis M. de","year":"2004","unstructured":"Luis M. de Campos, Juan M. Fern\u00e1ndez-Luna, and Juan F. Huete. 2004. Bayesian networks and information retrieval: An introduction to the special issue. Inf. Process. Manag. 40, 5 (2004), 727\u2013733.","journal-title":"Inf. Process. Manag."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1089\/106652700750050961"},{"key":"e_1_3_2_13_2","first-page":"1193","volume-title":"International Conference on Machine Learning (ICML\u201917)","author":"Gao Tian","year":"2017","unstructured":"Tian Gao, Kshitij Fadnis, and Murray Campbell. 2017. Local-to-global Bayesian network structure learning. In International Conference on Machine Learning (ICML\u201917). 1193\u20131202."},{"issue":"5","key":"e_1_3_2_14_2","first-page":"1169","article-title":"Efficient Markov blanket discovery and its application","volume":"47","author":"Gao Tian","year":"2016","unstructured":"Tian Gao and Qiang Ji. 2016. Efficient Markov blanket discovery and its application. IEEE Trans. Cybern. 47, 5 (2016), 1169\u20131179.","journal-title":"IEEE Trans. Cybern."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2016.09.009"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.00524"},{"key":"e_1_3_2_17_2","volume-title":"Computational Methods of Feature Selection","year":"2007","unstructured":"Isabelle Guyon, Constantin Aliferis, and Elisseeff Andre. 2007. Causal feature selection. In Computational Methods of Feature Selection. Chapman and Hall\/CRC, 79\u2013102."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-85066-3_3"},{"key":"e_1_3_2_19_2","first-page":"689","volume-title":"International Conference on Advances in Neural Information Processing Systems","author":"Hoyer Patrik O.","year":"2009","unstructured":"Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, and Bernhard Sch\u00f6lkopf. 2009. Nonlinear causal discovery with additive noise models. In International Conference on Advances in Neural Information Processing Systems. 689\u2013696."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2012.01.002"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.5555\/1795555"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3136625"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-14433-7"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Zhaolong Ling Kui Yu Hao Wang Lin Liu Wei Ding and Xindong Wu. 2019. BAMB: A balanced Markov blanket discovery approach to feature selection. ACM Transactions on Intelligent Systems and Technology (TIST) 10 5 (2019) 1\u201325.","DOI":"10.1145\/3335676"},{"key":"e_1_3_2_25_2","first-page":"505","volume-title":"International Conference on Advances in Neural Information Processing Systems","author":"Margaritis Dimitris","year":"2000","unstructured":"Dimitris Margaritis and Sebastian Thrun. 2000. Bayesian network induction via local neighborhoods. In International Conference on Advances in Neural Information Processing Systems. 505\u2013511."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946677"},{"key":"e_1_3_2_27_2","first-page":"634","volume-title":"UAI\u201912, Workshop on Causal Structure Learning, 2012","author":"Niinimki T.","year":"2012","unstructured":"T. Niinimki and Pekka Parviainen. 2012. Local structure discovery in Bayesian networks. In UAI\u201912, Workshop on Causal Structure Learning, 2012. 634\u2013643."},{"key":"e_1_3_2_28_2","volume-title":"Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference","author":"Pearl Judea","year":"2014","unstructured":"Judea Pearl. 2014. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.5555\/1390681.1442776"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2006.06.008"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.159"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Marco Scutari Claudia Vitolo and Allan Tucker. 2019. Learning bayesian networks from big data with greedy search: computational complexity and efficient implementation. Statistics and Computing 29 (2019) 1095\u20131108.","DOI":"10.1007\/s11222-019-09857-1"},{"key":"e_1_3_2_33_2","first-page":"2003","article-title":"A linear non-Gaussian acyclic model for causal discovery","volume":"7","author":"Shimizu Shohei","year":"2006","unstructured":"Shohei Shimizu, Patrik O. Hoyer, Aapo Hyv\u00e4rinen, and Antti Kerminen. 2006. A linear non-Gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7, Oct. (2006), 2003\u20132030.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_34_2","volume-title":"Causation, Prediction, and Search","author":"Spirtes Peter","year":"2000","unstructured":"Peter Spirtes, Clark N. Glymour, and Richard Scheines. 2000. Causation, Prediction, and Search. MIT Press."},{"key":"e_1_3_2_35_2","volume-title":"International Conference on Artificial Intelligence and Statistics (AISTATS\u201903)","author":"Tsamardinos Ioannis","year":"2003","unstructured":"Ioannis Tsamardinos and Constantin F. Aliferis. 2003. Towards principled feature selection: Relevancy, filters and wrappers. In International Conference on Artificial Intelligence and Statistics (AISTATS\u201903)."},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1145\/956750.956838","volume-title":"9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Tsamardinos Ioannis","year":"2003","unstructured":"Ioannis Tsamardinos, Constantin F. Aliferis, and Alexander Statnikov. 2003. Time and sample efficient discovery of Markov blankets and direct causal relations. In 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 673\u2013678."},{"key":"e_1_3_2_37_2","volume-title":"FLAIRS Conference","author":"Tsamardinos Ioannis","year":"2003","unstructured":"Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov, and Er Statnikov. 2003. Algorithms for large scale Markov blanket discovery. In FLAIRS Conference."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6889-7"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3409382"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2908373"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014149"},{"key":"e_1_3_2_42_2","first-page":"7154","volume-title":"International Conference on Machine Learning","author":"Yu Yue","year":"2019","unstructured":"Yue Yu, Jie Chen, Tian Gao, and Mo Yu. 2019. DAG-GNN: DAG structure learning with graph neural networks. In International Conference on Machine Learning. 7154\u20137163."},{"key":"e_1_3_2_43_2","article-title":"On the identifiability of the post-nonlinear causal model","author":"Zhang Kun","year":"2012","unstructured":"Kun Zhang and Aapo Hyvarinen. 2012. On the identifiability of the post-nonlinear causal model. arXiv preprint arXiv:1205.2599 (2012).","journal-title":"arXiv preprint arXiv:1205.2599"},{"issue":"1","key":"e_1_3_2_44_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1093\/nsr\/nwx137","article-title":"Learning causality and causality-related learning: Some recent progress","volume":"5","author":"Zhang Kun","year":"2018","unstructured":"Kun Zhang, Bernhard Sch\u00f6lkopf, Peter Spirtes, and Clark Glymour. 2018. Learning causality and causality-related learning: Some recent progress. Nat. Sci. Rev. 5, 1 (2018), 26\u201329.","journal-title":"Nat. Sci. Rev."},{"key":"e_1_3_2_45_2","first-page":"1588","volume-title":"International Conference on Advances in Neural Information Processing Systems","author":"Zhang Muhan","year":"2019","unstructured":"Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen. 2019. D-VAE: A variational autoencoder for directed acyclic graphs. In International Conference on Advances in Neural Information Processing Systems. 1588\u20131600."},{"key":"e_1_3_2_46_2","first-page":"9472","volume-title":"International Conference on Advances in Neural Information Processing Systems","author":"Zheng Xun","year":"2018","unstructured":"Xun Zheng, Bryon Aragam, Pradeep K. Ravikumar, and Eric P. Xing. 2018. DAGs with NO TEARS: Continuous optimization for structure learning. In International Conference on Advances in Neural Information Processing Systems. 9472\u20139483."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3624479","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3624479","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:49:45Z","timestamp":1750268985000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3624479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2,29]]}},"alternative-id":["10.1145\/3624479"],"URL":"https:\/\/doi.org\/10.1145\/3624479","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,13]]},"assertion":[{"value":"2022-05-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}