{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:36:32Z","timestamp":1760402192624,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T00:00:00Z","timestamp":1642032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hebei Natural Science Foundation of China,","award":["No.F2021203038"],"award-info":[{"award-number":["No.F2021203038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Streaming feature selection has always been an excellent method for selecting the relevant subset of features from high-dimensional data and overcoming learning complexity. However, little attention is paid to online feature selection through the Markov Blanket (MB). Several studies based on traditional MB learning presented low prediction accuracy and used fewer datasets as the number of conditional independence tests is high and consumes more time. This paper presents a novel algorithm called Online Feature Selection Via Markov Blanket (OFSVMB) based on a statistical conditional independence test offering high accuracy and less computation time. It reduces the number of conditional independence tests and incorporates the online relevance and redundant analysis to check the relevancy between the upcoming feature and target variable T, discard the redundant features from Parents-Child (PC) and Spouses (SP) online, and find PC and SP simultaneously. The performance OFSVMB is compared with traditional MB learning algorithms including IAMB, STMB, HITON-MB, BAMB, and EEMB, and Streaming feature selection algorithms including OSFS, Alpha-investing, and SAOLA on 9 benchmark Bayesian Network (BN) datasets and 14 real-world datasets. For the performance evaluation, F1, precision, and recall measures are used with a significant level of 0.01 and 0.05 on benchmark BN and real-world datasets, including 12 classifiers keeping a significant level of 0.01. On benchmark BN datasets with 500 and 5000 sample sizes, OFSVMB achieved significant accuracy than IAMB, STMB, HITON-MB, BAMB, and EEMB in terms of F1, precision, recall, and running faster. It finds more accurate MB regardless of the size of the features set. In contrast, OFSVMB offers substantial improvements based on mean prediction accuracy regarding 12 classifiers with small and large sample sizes on real-world datasets than OSFS, Alpha-investing, and SAOLA but slower than OSFS, Alpha-investing, and SAOLA because these algorithms only find the PC set but not SP. Furthermore, the sensitivity analysis shows that OFSVMB is more accurate in selecting the optimal features.<\/jats:p>","DOI":"10.3390\/sym14010149","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T03:14:56Z","timestamp":1642130096000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Online Streaming Features Selection via Markov Blanket"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9772-1167","authenticated-orcid":false,"given":"Waqar","family":"Khan","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7696-1412","authenticated-orcid":false,"given":"Lingfu","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brekhna","family":"Brekhna","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China"},{"name":"Department of Computer Science, Shaheed Benazir Bhutto University, Peshawar 25000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huigui","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wu, D., He, Y., Luo, X., and Zhou, M. (2021). A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection. IEEE Trans. Syst. Man Cybern. Syst.","DOI":"10.1109\/TSMC.2021.3096065"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1016\/j.asr.2019.07.017","article-title":"Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era","volume":"64","author":"DeLatte","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_3","unstructured":"Tsamardinos, I., and Aliferis, C. (2003, January 3\u20136). Towards Principled Feature Selection: Relevancy, Filters and Wrappers. Proceedings of the International Workshop on Artificial Intelligence and Statistics, Key West, FL, USA."},{"key":"ref_4","unstructured":"Aliferis, C., Tsamardinos, I., and Statnikov, A. (2003). HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection. Annual Symposium Proceedings. AMIA Symposium, AMIA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TCYB.2016.2539338","article-title":"Efficient Markov Blanket Discovery and Its Application","volume":"47","author":"Gao","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"52:1","DOI":"10.1145\/3335676","article-title":"BAMB: A Balanced Markov Blanket Discovery Approach to Feature Selection","volume":"10","author":"Ling","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ins.2019.09.010","article-title":"Towards efficient and effective discovery of Markov blankets for feature selection","volume":"509","author":"Wang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alnuaimi, N., Masud, M., Serhani, M.A., and Zaki, N. (2020). Streaming feature selection algorithms for big data: A survey. Appl. Comput. Inform.","DOI":"10.1016\/j.aci.2019.01.001"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s10115-018-1154-5","article-title":"Personalized recommendation with implicit feedback via learning pairwise preferences over item-sets","volume":"58","author":"Pan","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_10","unstructured":"Yang, S., Wang, H., and Hu, X. (2019). Efficient Local Causal Discovery Based on Markov Blanket. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sowmya, R., and Suneetha, K. (2017, January 5\u20136). Data mining with big data. Proceedings of the 2017 11th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India.","DOI":"10.1109\/ISCO.2017.7855990"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Boulesnane, A., and Meshoul, S. (2018). Effective Streaming Evolutionary Feature Selection Using Dynamic Optimization. IFIP International Conference on Computational Intelligence and Its Applications, Springer.","DOI":"10.1007\/978-3-319-89743-1_29"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhou, J., Foster, D., Stine, R., and Ungar, L. (2005, January 21\u201324). Streaming feature selection using alpha-investing. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in data Mining, Chicago, IL USA.","DOI":"10.1145\/1081870.1081914"},{"key":"ref_14","first-page":"1","article-title":"Scalable and accurate online feature selection for big data","volume":"11","author":"Yu","year":"2016","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_15","first-page":"1178","article-title":"Online feature selection with streaming features","volume":"35","author":"Wu","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3409382","article-title":"Causality-based feature selection: Methods and evaluations","volume":"53","author":"Yu","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_17","first-page":"6430","article-title":"Multi-label causal feature selection","volume":"34","author":"Wu","year":"2020","journal-title":"Proc. Aaai Conf. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102222","DOI":"10.1109\/ACCESS.2020.2998482","article-title":"Markov Boundary Learning With Streaming Data for Supervised Classification","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/TETCI.2020.2978238","article-title":"Using feature selection for local causal structure learning","volume":"5","author":"Ling","year":"2020","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107157","DOI":"10.1016\/j.knosys.2021.107157","article-title":"Online group streaming feature selection considering feature interaction","volume":"226","author":"Zhou","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_21","unstructured":"You, D., Wang, Y., Xiao, J., Lin, Y., Pan, M., Chen, Z., Shen, L., and Wu, X. (2021). Online Multi-label Streaming Feature Selection with Label Correlation. IEEE Trans. Knowl. Data Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, L., Lin, Y., Zhao, H., Chen, J., and Li, S. (2021). Causality-based online streaming feature selection. Concurrency and Computation: Practice and Experience, Wiley Online Library.","DOI":"10.1002\/cpe.6347"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"479","DOI":"10.2991\/ijcis.d.200423.002","article-title":"Online Streaming Feature Selection via Multi-Conditional Independence and Mutual Information Entropy\u2020","volume":"13","author":"Wang","year":"2020","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"You, D., Wu, X., Shen, L., He, Y., Yuan, X., Chen, Z., Deng, S., and Ma, C. (2018). Online Streaming Feature Selection via Conditional Independence. Appl. Sci., 8.","DOI":"10.3390\/app8122548"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Spirtes, P., Glymour, C., and Scheines, R. (1993). Causation, prediction, and search. Causation, Prediction, and Search, Springer.","DOI":"10.1007\/978-1-4612-2748-9"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"You, D., Li, R., Sun, M., Ou, X., Liang, S., and Yuan, F. (2020, January 9\u201311). Online Markov Blanket Discovery With Streaming Features. Proceedings of the 2020 IEEE International Conference on Knowledge Graph (ICKG), Nanjing, China.","DOI":"10.1109\/ICBK50248.2020.00023"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Singh, A., and Kumar, R. (2020, January 14\u201315). Heart Disease Prediction Using Machine Learning Algorithms. Proceedings of the 2020 International Conference on Electrical and Electronics Engineering (ICE3), Gorakhpur, India.","DOI":"10.1109\/ICE348803.2020.9122958"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shen, Z., Chen, X., and Garibaldi, J. (2021, January 7\u201310). Performance Optimization of a Fuzzy Entropy Based Feature Selection and Classification Framework. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00238"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, D., Luo, X., Shang, M., He, Y., Wang, G., and Wu, X. (2020). A data-characteristic-aware latent factor model for web services QoS prediction. IEEE Trans. Knowl. Data Eng.","DOI":"10.1109\/TKDE.2020.3014302"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2836236","DOI":"10.1155\/2020\/2836236","article-title":"The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets","volume":"2020","author":"Ucar","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_31","unstructured":"Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J. (2020). Open Graph Benchmark: Datasets for Machine Learning on Graphs. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1109\/TNNLS.2020.2981386","article-title":"Toward Mining Capricious Data Streams: A Generative Approach","volume":"32","author":"He","year":"2021","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5327","DOI":"10.1158\/1538-7445.AM2020-5327","article-title":"Abstract 5327: KPG-121, a novel CRBN modulator, potently inhibits growth of metastatic castration resistant prostate cancer as a single agent or in combination with androgen receptor signaling inhibitors both in vitro and in vivo","volume":"80","author":"Ge","year":"2020","journal-title":"Cancer Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). nuScenes: A Multimodal Dataset for Autonomous Driving. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/1\/149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:32Z","timestamp":1760362052000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/1\/149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,13]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["sym14010149"],"URL":"https:\/\/doi.org\/10.3390\/sym14010149","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2022,1,13]]}}}