{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:23:35Z","timestamp":1757618615343,"version":"3.44.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01225-3","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T06:33:47Z","timestamp":1751956427000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel sub-network level ensemble deep neural network with a regularized loss function to improve prediction performance"],"prefix":"10.1186","volume":"12","author":[{"given":"Jalil","family":"Toosifar","sequence":"first","affiliation":[]},{"given":"Yahya","family":"Forghani","sequence":"additional","affiliation":[]},{"given":"Seyyed Abed","family":"Hosseini","sequence":"additional","affiliation":[]},{"given":"Nasser","family":"Shoeibi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"1225_CR1","doi-asserted-by":"publisher","first-page":"105151","DOI":"10.1016\/j.engappai.2022.105151","volume":"115","author":"MA Ganaie","year":"2022","unstructured":"Ganaie MA, et al. Ensemble deep learning: A review. Eng Appl Artif Intell. 2022;115:105151.","journal-title":"Eng Appl Artif Intell"},{"issue":"4","key":"1225_CR2","first-page":"e1249","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi O, Rokach L. Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Min Knowl Discovery. 2018;8(4):e1249.","journal-title":"Wiley Interdisciplinary Reviews: Data Min Knowl Discovery"},{"key":"1225_CR3","unstructured":"Ali K, Pazzani M. On the link between error correlation and error reduction in decision tree ensembles (Technical Report ICSTR-95-38). Dept. of Information and Computer Science, UCI, USA, 1995."},{"key":"1225_CR4","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","volume":"239","author":"H Deng","year":"2013","unstructured":"Deng H, Runger G, Tuv E, Vladimir M. A time series forest for classification and feature extraction. Inf Sci. 2013;239:142\u201353.","journal-title":"Inf Sci"},{"key":"1225_CR5","doi-asserted-by":"crossref","unstructured":"Chan PK, Stolfo SJ. A comparative evaluation of voting and meta-learning on partitioned data, in Machine Learning Proceedings 1995. 1995, Elsevier. pp. 90\u201398.","DOI":"10.1016\/B978-1-55860-377-6.50020-7"},{"key":"1225_CR6","doi-asserted-by":"crossref","unstructured":"Lin, C., et al., Multi-model ensemble learning for battery state-of-health estimation: Recent advances and perspectives. J Energy Chem, 2024;100:739\u201359.","DOI":"10.1016\/j.jechem.2024.09.021"},{"key":"1225_CR7","doi-asserted-by":"publisher","first-page":"109101","DOI":"10.1016\/j.ast.2024.109101","volume":"148","author":"L-K Song","year":"2024","unstructured":"Song L-K, Li X-Q, Zhu S-P, Choy Y-S. Cascade ensemble learning for multi-level reliability evaluation. Aerosp Sci Technol. 2024;148:109101.","journal-title":"Aerosp Sci Technol"},{"issue":"16","key":"1225_CR8","doi-asserted-by":"publisher","first-page":"48521","DOI":"10.1007\/s11042-023-17415-1","volume":"83","author":"A Shankar","year":"2024","unstructured":"Shankar A, et al. An intelligent recommendation system in e-commerce using ensemble learning. Multimedia Tools Appl. 2024;83(16):48521\u201337.","journal-title":"Multimedia Tools Appl"},{"key":"1225_CR9","doi-asserted-by":"crossref","unstructured":"Musaev J, et al. ICNN-Ensemble: an improved convolutional neural network ensemble model for medical image classification. IEEE Access; 2023.","DOI":"10.1109\/ACCESS.2023.3303966"},{"key":"1225_CR10","doi-asserted-by":"crossref","unstructured":"Antonio B, Moroni D, Martinelli M. Efficient adaptive ensembling for image classification. Expert Systems, 2023;42(1).","DOI":"10.1111\/exsy.13424"},{"key":"1225_CR11","doi-asserted-by":"publisher","first-page":"109579","DOI":"10.1016\/j.ijepes.2023.109579","volume":"155","author":"MY Junior","year":"2024","unstructured":"Junior MY, et al. Optimized hybrid ensemble learning approaches applied to very short-term load forecasting. Int J Electr Power Energy Syst. 2024;155:109579.","journal-title":"Int J Electr Power Energy Syst"},{"key":"1225_CR12","unstructured":"Maji D, Santara A, Mitra P, Sheet D. Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images. arXiv preprint arXiv:1603.04833, 2016."},{"issue":"1","key":"1225_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-018-0286-0","volume":"18","author":"H Jung","year":"2018","unstructured":"Jung H, et al. Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method. BMC Med Imaging. 2018;18(1):1\u201310.","journal-title":"BMC Med Imaging"},{"issue":"6","key":"1225_CR14","doi-asserted-by":"publisher","first-page":"3310","DOI":"10.1016\/j.jksuci.2022.03.023","volume":"34","author":"IU Haq","year":"2022","unstructured":"Haq IU, et al. Feature fusion and ensemble learning-based CNN model for mammographic image classification. J King Saud University-Computer Inform Sci. 2022;34(6):3310\u20138.","journal-title":"J King Saud University-Computer Inform Sci"},{"key":"1225_CR15","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.knosys.2019.03.016","volume":"175","author":"W Zhang","year":"2019","unstructured":"Zhang W, et al. Automated identification and grading system of diabetic retinopathy using deep neural networks. Knowl Based Syst. 2019;175:12\u201325.","journal-title":"Knowl Based Syst"},{"issue":"1","key":"1225_CR16","doi-asserted-by":"publisher","first-page":"148","DOI":"10.3390\/electronics11010148","volume":"11","author":"M Sharma","year":"2022","unstructured":"Sharma M, et al. Ensemble averaging of transfer learning models for identification of nutritional deficiency in rice plant. Electronics. 2022;11(1):148.","journal-title":"Electronics"},{"key":"1225_CR17","doi-asserted-by":"crossref","unstructured":"Jiang W et al. Model level ensemble for facial action unit recognition at the 3rd ABAW challenge. in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022.","DOI":"10.1109\/CVPRW56347.2022.00260"},{"key":"1225_CR18","doi-asserted-by":"crossref","unstructured":"Khan W, et al. Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks. Data Science and Management; 2024.","DOI":"10.1016\/j.dsm.2024.09.002"},{"issue":"2","key":"1225_CR19","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.dsm.2023.10.005","volume":"7","author":"W Khan","year":"2024","unstructured":"Khan W, et al. Anomalous node detection in attributed social networks using dual variational autoencoder with generative adversarial networks. Data Sci Manage. 2024;7(2):89\u201398.","journal-title":"Data Sci Manage"},{"issue":"1","key":"1225_CR20","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929\u201358.","journal-title":"J Mach Learn Res"},{"key":"1225_CR21","doi-asserted-by":"crossref","unstructured":"Dietterich TG. Ensemble methods in machine learning. in International workshop on multiple classifier systems. 2000. Springer.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"1225_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach L. Ensemble-based classifiers. Artif Intell Rev. 2010;33:1\u201339.","journal-title":"Artif Intell Rev"},{"key":"1225_CR23","doi-asserted-by":"crossref","unstructured":"Zhou Z-H. Ensemble methods: foundations and algorithms. CRC; 2012.","DOI":"10.1201\/b12207"},{"key":"1225_CR24","doi-asserted-by":"crossref","unstructured":"Leckie C, Zukerman I. Learning search control rules for planning: An inductive approach, in Machine Learning Proceedings 1991. 1991, Elsevier. pp. 422\u2013426.","DOI":"10.1016\/B978-1-55860-200-7.50087-8"},{"issue":"2","key":"1225_CR25","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3390\/bdcc9020039","volume":"9","author":"AW Abulfaraj","year":"2025","unstructured":"Abulfaraj AW, Binzagr F. A deep ensemble learning approach based on a vision transformer and neural network for Multi-Label image classification. Big Data Cogn Comput. 2025;9(2):39.","journal-title":"Big Data Cogn Comput"},{"key":"1225_CR26","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1023\/A:1018054314350","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L. Bagging predictors. Mach Learn. 1996;24:123\u201340.","journal-title":"Mach Learn"},{"key":"1225_CR27","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"1225_CR28","unstructured":"Schapire RE. A brief Introduction to boosting. In Ijcai. Citeseer; 1999."},{"key":"1225_CR29","unstructured":"Freund Y, Schapire RE. Experiments with a new boosting algorithm. in icml. 1996. Citeseer."},{"key":"1225_CR30","doi-asserted-by":"crossref","unstructured":"Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat, 2001;29(5): pp. 1189\u2013232.","DOI":"10.1214\/aos\/1013203451"},{"key":"1225_CR31","doi-asserted-by":"crossref","unstructured":"Liu P, Han S, Meng Z, Tong Y. Facial expression recognition via a boosted deep belief network. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.","DOI":"10.1109\/CVPR.2014.233"},{"key":"1225_CR32","unstructured":"Yasin M, Salman A, Masood H, Maqbool A. Optimizing CNN-Based Image Classification with Ensemble Models and Transfer Learning."},{"issue":"2","key":"1225_CR33","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH. Stacked generalization. Neural Netw. 1992;5(2):241\u201359.","journal-title":"Neural Netw"},{"key":"1225_CR34","doi-asserted-by":"crossref","unstructured":"Deng L, Yu D. Deep convex net: A scalable architecture for speech pattern classification. in Twelfth annual conference of the international speech communication association. 2011.","DOI":"10.21437\/Interspeech.2011-607"},{"key":"1225_CR35","doi-asserted-by":"crossref","unstructured":"Hutchinson B, Deng L, Yu D. A deep architecture with bilinear modeling of hidden representations: Applications to phonetic recognition. in 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP). 2012. IEEE.","DOI":"10.1109\/ICASSP.2012.6288994"},{"issue":"10","key":"1225_CR36","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1016\/S0893-6080(99)00073-8","volume":"12","author":"Y Liu","year":"1999","unstructured":"Liu Y, Yao X. Ensemble learning via negative correlation. Neural Netw. 1999;12(10):1399\u2013404.","journal-title":"Neural Netw"},{"key":"1225_CR37","doi-asserted-by":"crossref","unstructured":"Shi Z et al. Crowd counting with deep negative correlation learning. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.","DOI":"10.1109\/CVPR.2018.00564"},{"issue":"3\u20134","key":"1225_CR38","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/0370-2693(93)90272-J","volume":"299","author":"B Mele","year":"1993","unstructured":"Mele B, Altarelli G. Lepton spectra as a measure of b quark polarization at LEP. Phys Lett B. 1993;299(3\u20134):345\u201350.","journal-title":"Phys Lett B"},{"key":"1225_CR39","unstructured":"Lee C-Y, Gallagher PW, Tu Z. Generalizing pooling functions In convolutional neural networks: mixed, gated, and tree. In Artificial Intelligence and statistics. PMLR; 2016."},{"issue":"1","key":"1225_CR40","doi-asserted-by":"publisher","first-page":"112102","DOI":"10.1007\/s11432-018-9944-x","volume":"63","author":"W Zhang","year":"2020","unstructured":"Zhang W, Jiang J, Shao Y, Cui B. Snapshot boosting: a fast ensemble framework for deep neural networks. Sci China Inform Sci. 2020;63(1):112102.","journal-title":"Sci China Inform Sci"},{"issue":"9","key":"1225_CR41","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s42256-020-0217-y","volume":"2","author":"Y Cao","year":"2020","unstructured":"Cao Y, Geddes TA, Yang JYH, Yang P. Ensemble deep learning in bioinformatics. Nat Mach Intell. 2020;2(9):500\u20138.","journal-title":"Nat Mach Intell"},{"issue":"2","key":"1225_CR42","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.1007\/s10489-022-03689-9","volume":"53","author":"F Younas","year":"2023","unstructured":"Younas F, Usman M, Yan WQ. A deep ensemble learning method for colorectal polyp classification with optimized network parameters. Appl Intell. 2023;53(2):2410\u201333.","journal-title":"Appl Intell"},{"key":"1225_CR43","doi-asserted-by":"crossref","unstructured":"Nguyen TT et al. Multi-layer heterogeneous ensemble with classifier and feature selection. in Proceedings of the 2020 Genetic and Evolutionary Computation Conference. 2020.","DOI":"10.1145\/3377930.3389832"},{"key":"1225_CR44","doi-asserted-by":"crossref","unstructured":"Dogan A, Birant D. A weighted majority voting ensemble approach for classification. in 2019 4th International Conference on Computer Science and Engineering (UBMK). 2019. IEEE.","DOI":"10.1109\/UBMK.2019.8907028"},{"key":"1225_CR45","doi-asserted-by":"crossref","unstructured":"Van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol, 2007. 6(1).","DOI":"10.2202\/1544-6115.1309"},{"key":"1225_CR46","unstructured":"Chen H, Lundberg S, Lee S-I. Checkpoint ensembles: Ensemble methods from a single training process. arXiv preprint arXiv:1710.03282, 2017."},{"key":"1225_CR47","doi-asserted-by":"publisher","first-page":"217499","DOI":"10.1109\/ACCESS.2020.3041525","volume":"8","author":"J Yang","year":"2020","unstructured":"Yang J, Wang F. Auto-ensemble: an adaptive learning rate scheduling based deep learning model ensembling. IEEE Access. 2020;8:217499\u2013509.","journal-title":"IEEE Access"},{"issue":"8","key":"1225_CR48","first-page":"1019","volume":"23","author":"KS Yoon","year":"2020","unstructured":"Yoon KS, Choi JY. Compressed ensemble of deep convolutional neural networks with global and local facial features for improved face recognition. J Korea Multimedia Soc. 2020;23(8):1019\u201329.","journal-title":"J Korea Multimedia Soc"},{"key":"1225_CR49","doi-asserted-by":"publisher","first-page":"107978","DOI":"10.1016\/j.patcog.2021.107978","volume":"117","author":"Q Shi","year":"2021","unstructured":"Shi Q, Katuwal R, Suganthan PN, Tanveer M. Random vector functional link neural network based ensemble deep learning. Pattern Recogn. 2021;117:107978.","journal-title":"Pattern Recogn"},{"key":"1225_CR50","doi-asserted-by":"crossref","unstructured":"Khan W, Ebrahim N. ANOGAT-Sparse-TL: A hybrid framework combining sparsification and graph attention for anomaly detection in attributed networks using the optimized loss function incorporating the Twersky loss for improved robustness. Knowl Based Syst, 2025;311: p. 113144.","DOI":"10.1016\/j.knosys.2025.113144"},{"issue":"7","key":"1225_CR51","doi-asserted-by":"publisher","first-page":"19929","DOI":"10.1007\/s11042-023-15607-3","volume":"83","author":"JJ Valero-Mas","year":"2024","unstructured":"Valero-Mas JJ, Gallego AJ, Rico-Juan JR. An overview of ensemble and feature learning in few-shot image classification using Siamese networks. Multimedia Tools Appl. 2024;83(7):19929\u201352.","journal-title":"Multimedia Tools Appl"},{"issue":"3","key":"1225_CR52","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TPAMI.2019.2943860","volume":"43","author":"L Zhang","year":"2019","unstructured":"Zhang L, et al. Nonlinear regression via deep negative correlation learning. IEEE Trans Pattern Anal Mach Intell. 2019;43(3):982\u201398.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1225_CR53","unstructured":"Yang Z et al. Rethinking bias-variance trade-off for generalization of neural networks. in International Conference on Machine Learning. 2020. PMLR."},{"key":"1225_CR54","first-page":"12","volume":"1050","author":"AM Webb","year":"2019","unstructured":"Webb AM, et al. Joint training of neural network ensembles. Stat. 2019;1050:12.","journal-title":"Stat"},{"key":"1225_CR55","doi-asserted-by":"crossref","unstructured":"Imambi S, Prakash KB, Kanagachidambaresan G. PyTorch. Programming with TensorFlow: Solution for Edge Computing Applications, 2021: pp. 87\u2013104.","DOI":"10.1007\/978-3-030-57077-4_10"},{"key":"1225_CR56","unstructured":"Krizhevsky A, Nair V, Hinton G. The CIFAR-10 dataset. online: http:\/\/www.cs.toronto.edu\/kriz\/cifar.html, 55, 2014. Cited on pages 73, 117, and. 120."},{"key":"1225_CR57","unstructured":"LeCun Y. The MNIST database of handwritten digits.http:\/\/yann.lecun.com\/exdb\/mnist\/, 1998."},{"key":"1225_CR58","unstructured":"zalando. Available from: https:\/\/www.kaggle.com\/datasets\/zalando-research\/fashionmnist"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01225-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01225-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01225-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T01:28:37Z","timestamp":1757208517000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01225-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1225"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01225-3","relation":{},"ISSN":["2196-1115"],"issn-type":[{"type":"electronic","value":"2196-1115"}],"subject":[],"published":{"date-parts":[[2025,7,8]]},"assertion":[{"value":"22 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics and consent to publish declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"155"}}