{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:28:50Z","timestamp":1772119730532,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mobile Netw Appl"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s11036-024-02341-9","type":"journal-article","created":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T21:01:20Z","timestamp":1719176480000},"page":"905-921","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Development of an Intelligent Virtualization Platform Key Metrics Monitoring System: Collaborative Implementation with Self-Training and Bagging Algorithm"],"prefix":"10.1007","volume":"29","author":[{"given":"Ruey-Chyi","family":"Wu","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"2341_CR1","unstructured":"Zhu Xiaojin (2008) Semi-supervised learning literature survey. Technical report TR1530. University of Wisconsin-Madison Department of Computer Sciences.\u00a0https:\/\/pages.cs.wisc.edu\/~jerryzhu\/pub\/ssl_survey.pdf. Accessed\u00a02\u00a0Jan 2024"},{"key":"2341_CR2","doi-asserted-by":"publisher","unstructured":"Sharma K, Nandal R (2019) A Literature Study On Machine Learning Fusion With IoT. In: 2019 3rd International conference on trends in electronics and informatics (ICOEI), Tirunelveli, India (pp 1440\u20131445). https:\/\/doi.org\/10.1109\/ICOEI.2019.8862656","DOI":"10.1109\/ICOEI.2019.8862656"},{"issue":"5","key":"2341_CR3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/MWC.2016.7721736","volume":"23","author":"E Ahmed","year":"2016","unstructured":"Ahmed E, Yaqoob I, Gani A, Imran M, Guizani M (2016) Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wirel Commun 23(5):10\u201316. https:\/\/doi.org\/10.1109\/MWC.2016.7721736","journal-title":"IEEE Wirel Commun"},{"key":"2341_CR4","doi-asserted-by":"publisher","unstructured":"Sharma SK, Wang X (2017) Live data analytics with collaborative edge and cloud processing in wireless IoT Networks. IEEE Access 1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2017.2682640","DOI":"10.1109\/ACCESS.2017.2682640"},{"key":"2341_CR5","unstructured":"Kumar S (2021) Use Voting Classifier to Improve the Performance of Your ML Model: Essential Guide to Voting Classifier Ensemble. In Towards Data Science. https:\/\/towardsdatascience.com\/use-voting-classifier-to-improve-the-performance-of-your-ml-model-805345f9de0e.\u00a0Accessed\u00a02\u00a0Jan 2024"},{"issue":"21","key":"2341_CR6","doi-asserted-by":"publisher","first-page":"12291","DOI":"10.3390\/su132112291","volume":"13","author":"Wu Li-Ya","year":"2021","unstructured":"Li-Ya Wu, Weng S-S (2021) Ensemble learning models for food safety risk prediction. Sustainability 13(21):12291. https:\/\/doi.org\/10.3390\/su132112291","journal-title":"Sustainability"},{"key":"2341_CR7","unstructured":"Huang T (2018) Machine Learning: Bagging, Boosting, and AdaBoost in Ensemble Learning. Medium.\u00a0https:\/\/chih-sheng-huang821.medium.com\/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-ensemble-learning%E4%B9%8Bbagging-boosting%E5%92%8Cadaboost-af031229ebc3.\u00a0Accessed\u00a02\u00a0Jan 2024"},{"issue":"11","key":"2341_CR8","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.3390\/diagnostics12112595","volume":"12","author":"A Khan","year":"2022","unstructured":"Khan A, Khan A, Khan MM, Farid K, Alam MM, Suud MBM (2022) Cardiovascular and diabetes diseases classification using ensemble stacking classifiers with SVM as a meta classifier. Diagnostics (Basel) 12(11):2595. https:\/\/doi.org\/10.3390\/diagnostics12112595","journal-title":"Diagnostics (Basel)"},{"key":"2341_CR9","doi-asserted-by":"publisher","unstructured":"Reda Yacouby, Dustin Axman (2020) Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. In Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems (pp 79\u201391). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.eval4nlp-1.9","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"key":"2341_CR10","unstructured":"Kyriakides G, Margaritis KG (2019) Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras. Packt Publishing Company, Inc"},{"key":"2341_CR11","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, M\u00fcller A, Nothman J, Louppe G, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay \u00c9 (2011) Scikit-learn: Machine Learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"2341_CR12","volume-title":"Statistical Foundations of Machine Learning: Core Technologies Behind Deep Learning","author":"Y Zhisheng","year":"2021","unstructured":"Zhisheng Y (2021) Statistical Foundations of Machine Learning: Core Technologies Behind Deep Learning. Flag Technology Publishing"},{"key":"2341_CR13","unstructured":"Raschka S (2015) Python Machine Learning: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics. Packt Publishing Ltd, 169\u2013198"},{"key":"2341_CR14","unstructured":"Hastie T, Tibshirani R, Friedman J (2013) The Elements of Statistical Learning (2nd ed). Springer"},{"issue":"2","key":"2341_CR15","doi-asserted-by":"publisher","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","volume":"1168","author":"X Ying","year":"2019","unstructured":"Ying X (2019) An Overview of Overfitting and its Solutions. J Phys: Conf Ser 1168(2):022022. https:\/\/doi.org\/10.1088\/1742-6596\/1168\/2\/022022","journal-title":"J Phys: Conf Ser"},{"issue":"9","key":"2341_CR16","doi-asserted-by":"publisher","first-page":"1722","DOI":"10.1109\/TKDE.2019.2911585","volume":"32","author":"A Ghasemian","year":"2020","unstructured":"Ghasemian A, Hosseinmardi H, Clauset A (2020) Evaluating overfit and underfit in models of network community structure. IEEE Trans Knowl Data Eng 32(9):1722\u20131735. https:\/\/doi.org\/10.1109\/TKDE.2019.2911585","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2341_CR17","doi-asserted-by":"crossref","unstructured":"Liu B, Shen W, Li P, Zhu X (2019) Accelerate mini-batch machine learning training with dynamic batch size fitting. 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, pp. 1\u20138.\u00a0https:\/\/sci-hub.se\/10.1109\/IJCNN.2019.8851944.\u00a0Accessed\u00a02\u00a0Jan 2024","DOI":"10.1109\/IJCNN.2019.8851944"},{"issue":"10.3850","key":"2341_CR18","doi-asserted-by":"publisher","first-page":"978","DOI":"10.3850\/978-981-09-5247-1_017","volume":"70","author":"RZ Khan","year":"2015","unstructured":"Khan RZ, Khan RZ (2015) Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study). Comput Sci Commun Instrum Devices 70(10.3850):978\u2013981. https:\/\/doi.org\/10.3850\/978-981-09-5247-1_017","journal-title":"Comput Sci Commun Instrum Devices"},{"key":"2341_CR19","unstructured":"Cobbe K, Klimov O, Hesse C, Kim T, Schulman J (2019) Quantifying Generalization in Reinforcement Learning. In: Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1282\u20131289"},{"key":"2341_CR20","doi-asserted-by":"publisher","unstructured":"Hu W, Li Z, Yu D (2020) Simple and effective regularization methods for training on noisily labeled data with generalization guarantee. international conference on learning representations (ICLR) 2020. https:\/\/doi.org\/10.48550\/arXiv.1905.11368","DOI":"10.48550\/arXiv.1905.11368"},{"key":"2341_CR21","doi-asserted-by":"publisher","first-page":"4919","DOI":"10.1038\/s41467-022-32550-3","volume":"13","author":"MC Caro","year":"2022","unstructured":"Caro MC, Hsin-Yuan Huang M, Cerezo KS, Sornborger A, Cincio L, Coles PJ (2022) Generalization in quantum machine learning from few training data. Nat Commun 13:4919. https:\/\/doi.org\/10.1038\/s41467-022-32550-3","journal-title":"Nat Commun"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-024-02341-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-024-02341-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-024-02341-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T14:07:29Z","timestamp":1735654049000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-024-02341-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["2341"],"URL":"https:\/\/doi.org\/10.1007\/s11036-024-02341-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3830914\/v1","asserted-by":"object"}]},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"value":"1383-469X","type":"print"},{"value":"1572-8153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"3 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author declares that he has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}