{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:07Z","timestamp":1750309507830,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100005372","name":"Guilin University of Electronic Technology","doi-asserted-by":"publisher","award":["2024YCXS067"],"award-info":[{"award-number":["2024YCXS067"]}],"id":[{"id":"10.13039\/501100005372","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,6]]},"DOI":"10.1145\/3709026.3709031","type":"proceedings-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T10:05:41Z","timestamp":1739613941000},"page":"482-490","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal Feature Selection Algorithm Based on Maximizing Neighbourhood Mutual Information"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0589-3766","authenticated-orcid":false,"given":"Lijuan","family":"Hu","sequence":"first","affiliation":[{"name":"Guilin University Of Electronic Technology, Guilin, GuangXi, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0176-204X","authenticated-orcid":false,"given":"Zhuoyuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Guilin University Of Electronic Technology, Guilin, GuangXi, China"}]}],"member":"320","published-online":{"date-parts":[[2025,2,15]]},"reference":[{"key":"e_1_3_3_1_1_2","doi-asserted-by":"crossref","unstructured":"P S P G. Ensemble Feature Selection Framework for Paddy Yield Prediction in Cauvery Basin using Machine Learning Classifiers[J]. Cogent Engineering 2023 10(2):","DOI":"10.1080\/23311916.2023.2250061"},{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Mustapha S D F M S. Predictive Analysis of Students\u2019 Learning Performance Using Data Mining Techniques: A Comparative Study of Feature Selection Methods[J]. Applied System Innovation 2023 6(5):","DOI":"10.3390\/asi6050086"},{"key":"e_1_3_3_1_3_2","first-page":"151007352","article-title":"Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores#13;[J]","volume":"2022","author":"Shiheng L","unstructured":"Shiheng L, Hui W, Jian Z. Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores#13;[J]. Frontiers in Molecular Neuroscience,2022,151007352-1007352.","journal-title":"Frontiers in Molecular Neuroscience"},{"key":"e_1_3_3_1_4_2","volume-title":"The Book of Why[M]","author":"Pearl","year":"2014","unstructured":"Pearl. The Book of Why[M]. CITIC Publishing Group, Jiang sheng, Yu hua, 2014,18-19."},{"key":"e_1_3_3_1_5_2","volume-title":"Multi-source causal feature selection[J]","author":"Yu K","year":"2019","unstructured":"Yu K, Liu L, Li J, et al. Multi-source causal feature selection[J]. IEEE transactions on pattern analysis and machine intelligence, 2019, 42(9): 2240-2256"},{"key":"e_1_3_3_1_6_2","volume-title":"Software Journal","author":"Zhong Kunhua","year":"2022","unstructured":"Zhong Kunhua, Qin Xiaolin, Chen Min, et al. Causal feature discovery and prediction based on causal generative neural networks[J]. Software Journal, 2022."},{"key":"e_1_3_3_1_7_2","volume-title":"51","author":"Chen","year":"2022","unstructured":"Chen, YuWen, Ju Zhang, and **aoLin Qin. \"Interpretable instance disease prediction based on causal feature selection and effect analysis.\" BMC medical informatics and decision making 22.1 (2022): 51."},{"issue":"5","key":"e_1_3_3_1_8_2","first-page":"422","article-title":"A review of causal feature selection algorithms based on Markov boundary discovery[J]","volume":"35","author":"Xingyu Wu","unstructured":"Xingyu Wu, Bingbing Jiang, Shengfei Lu, et al. A review of causal feature selection algorithms based on Markov boundary discovery[J]. Pattern Recognition and Artificial Intelligence, 35(5): 422-438.","journal-title":"Pattern Recognition and Artificial Intelligence"},{"key":"e_1_3_3_1_9_2","first-page":"1","volume-title":"ACM Computing Surveys (CSUR)","author":"Yu K","year":"2020","unstructured":"Yu K, Guo X, Liu L, et al. Causality-based feature selection: Methods and evaluations[J]. ACM Computing Surveys (CSUR), 2020, 53(5): 1-36."},{"key":"e_1_3_3_1_10_2","first-page":"122","article-title":"Three-Fast-Inter Incremental Association Markov Blanket learning algorithm[J]","volume":"2019","author":"Xianglin Yang","unstructured":"Xianglin Yang, Yujing Wang, Yang Ou, Yunhai Tong. Three-Fast-Inter Incremental Association Markov Blanket learning algorithm[J]. Pattern Recognition Letters,2019,122.","journal-title":"Pattern Recognition Letters"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.05.102"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Chunlei S Xianwei X Jiacai Z. A novel multigranularity feature-selection method based on neighborhood mutual information and its application in autistic patient identification[J]. Biomedical Signal Processing and Control 2022 78","DOI":"10.1016\/j.bspc.2022.103887"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.11.007"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2017.12.152"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2019.08.004"},{"key":"e_1_3_3_1_16_2","unstructured":"KOLLER D SAHAMI M. Toward optimal feature selection[ C]\/\/Proceedings of ICML 1996: 1-14."},{"key":"e_1_3_3_1_17_2","first-page":"505","article-title":"network induction via local neighborhoods[J]","volume":"2000","author":"Bayesian","unstructured":"MARGARITIS D, THRUN S.Bayesian network induction via local neighborhoods[J]. Advances in Neural Information Processing Systems,2000, 12:505-511.","journal-title":"Advances in Neural Information Processing Systems"},{"volume-title":"Algorithms for large scale Markov blanket discovery [C]\/\/International Flairs Conference","year":"2003","key":"e_1_3_3_1_18_2","unstructured":"TSAMARDINOS I, ALIFERIS C F, STATNIKOV A R . Algorithms for large scale Markov blanket discovery [C]\/\/International Flairs Conference, 2003:376-380."},{"volume-title":"PMLR","author":"Tsamardinos I","key":"e_1_3_3_1_19_2","unstructured":"Tsamardinos I, Aliferis C F. Towards principled feature selection: Relevancy, filters and wrappers[C]\/\/International Workshop on Artificial Intelligence and Statistics. PMLR, 2003: 300-307."},{"volume-title":"A Fast Markov Blanket Discovery Algorithm[C]\/\/.Proceedings of 2014 IEEE 5th International Conference on Software Engineering and Service Science.2014:314-318","author":"Xiaofeng Zhu","key":"e_1_3_3_1_20_2","unstructured":"Xiaofeng Zhu, Youlong Yang. A Fast Markov Blanket Discovery Algorithm[C]\/\/.Proceedings of 2014 IEEE 5th International Conference on Software Engineering and Service Science.2014:314-318."},{"key":"e_1_3_3_1_21_2","volume-title":"Speculative Markov blanket discovery for optimal feature selection[C]\/\/Fifth IEEE International Conference on Data Mining (ICDM'05)","author":"Yaramakala S","year":"2005","unstructured":"Yaramakala S, Margaritis D. Speculative Markov blanket discovery for optimal feature selection[C]\/\/Fifth IEEE International Conference on Data Mining (ICDM'05). IEEE, 2005: 4 pp."},{"key":"e_1_3_3_1_22_2","first-page":"122","article-title":"Three-Fast-Inter Incremental Association Markov Blanket learning algorithm[J]","volume":"2019","author":"Xianglin Yang","unstructured":"Xianglin Yang, Yujing Wang, Yang Ou, Yunhai Tong. Three-Fast-Inter Incremental Association Markov Blanket learning algorithm[J]. Pattern Recognition Letters,2019,122.","journal-title":"Pattern Recognition Letters"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.5555\/3322706.3322714"},{"key":"e_1_3_3_1_24_2","volume-title":"A greedy feature selection algorithm for Big Data of high dimensionality[J]. Machine learning","author":"Tsamardinos I","year":"2019","unstructured":"Tsamardinos I, Borboudakis G, Katsogridakis P, et al. A greedy feature selection algorithm for Big Data of high dimensionality[J]. Machine learning, 2019, 108: 149-202."},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Tsamardinos I Aliferis C F Statnikov A. Time and sample efficient discovery of Markov blankets and direct causal relations[C]\/\/Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. 2003: 673-678.","DOI":"10.1145\/956750.956838"},{"issue":"1","key":"e_1_3_3_1_27_2","article-title":"Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation[J]","volume":"11","author":"Aliferis C F","year":"2010","unstructured":"Aliferis C F, Statnikov A, Tsamardinos I, et al. Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation[J]. Journal of Machine Learning Research, 2010, 11(1).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2006.06.008"},{"key":"e_1_3_3_1_29_2","first-page":"96","volume-title":"Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings 21","author":"Fu S","year":"2008","unstructured":"Fu S, Desmarais M C. Fast Markov blanket discovery algorithm via local learning within single pass[C]\/\/Advances in Artificial Intelligence: 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings 21. Springer Berlin Heidelberg, 2008: 96-107."},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3335676"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.09.010"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3218786"},{"key":"e_1_3_3_1_33_2","volume-title":"Efficient Markov blanket discovery and its application[J]","author":"Gao T","year":"2016","unstructured":"Gao T, Ji Q. Efficient Markov blanket discovery and its application[J]. IEEE transactions on Cybernetics, 2016, 47(5): 1169-1179."},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.12.118"}],"event":{"name":"CSAI 2024: 2024 8th International Conference on Computer Science and Artificial Intelligence (CSAI)","acronym":"CSAI 2024","location":"Beijing China"},"container-title":["Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3709026.3709031","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3709026.3709031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:55Z","timestamp":1750295875000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3709026.3709031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"references-count":33,"alternative-id":["10.1145\/3709026.3709031","10.1145\/3709026"],"URL":"https:\/\/doi.org\/10.1145\/3709026.3709031","relation":{},"subject":[],"published":{"date-parts":[[2024,12,6]]},"assertion":[{"value":"2025-02-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}