{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:12:20Z","timestamp":1773249140906,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:00:00Z","timestamp":1625270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:00:00Z","timestamp":1625270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Major Science and Technology Program of Xinjiang Production and Construction Corps","award":["2021AA006"],"award-info":[{"award-number":["2021AA006"]}]},{"name":"Natural Science Program of Shihezi University","award":["KX01230101"],"award-info":[{"award-number":["KX01230101"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00530-021-00827-0","type":"journal-article","created":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T03:35:27Z","timestamp":1625283327000},"page":"2843-2851","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Few-shot imbalanced classification based on data augmentation"],"prefix":"10.1007","volume":"29","author":[{"given":"Xuewei","family":"Chao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8367-6935","authenticated-orcid":false,"given":"Lixin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,3]]},"reference":[{"key":"827_CR1","doi-asserted-by":"publisher","first-page":"105240","DOI":"10.1016\/j.compag.2020.105240","volume":"169","author":"Y Li","year":"2020","unstructured":"Li, Y., Yang, J.: Few-shot cotton pest recognition and terminal realization. Comput Electron Agric 169, 105240 (2020)","journal-title":"Comput Electron Agric"},{"key":"827_CR2","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw 106, 249\u2013259 (2018)","journal-title":"Neural Netw"},{"key":"827_CR3","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang, G., Yijing, L., Shang, J., et al.: Learning from class-imbalanced data: Review of methods and applications. Expert Syst Appl 73, 220\u2013239 (2017)","journal-title":"Expert Syst Appl"},{"key":"827_CR4","doi-asserted-by":"crossref","unstructured":"Kumar G, Thakur K, Ayyagari M R. MLEsIDSs: machine learning-based ensembles for intrusion detection systems\u2014a review. J Supercomput. 2020: 1\u201334.","DOI":"10.1007\/s11227-020-03196-z"},{"key":"827_CR5","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.ast.2018.08.042","volume":"84","author":"PP Xi","year":"2019","unstructured":"Xi, P.P., Zhao, Y.P., Wang, P.X., et al.: Least squares support vector machine for class imbalance learning and their applications to fault detection of aircraft engine. Aerosp Sci Technol 84, 56\u201374 (2019)","journal-title":"Aerosp Sci Technol"},{"key":"827_CR6","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.inffus.2017.09.005","volume":"41","author":"F Carcillo","year":"2018","unstructured":"Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.A., et al.: Scarff: a scalable framework for streaming credit card fraud detection with spark. Information Fusion 41, 182\u2013194 (2018)","journal-title":"Information Fusion"},{"issue":"12","key":"827_CR7","first-page":"1675","volume":"74","author":"X Sheng","year":"2016","unstructured":"Sheng, X., Li, Y., Lian, M., et al.: Influence of coupling interference on arrayed eddy current displacement measurement. Mater Eval 74(12), 1675\u20131683 (2016)","journal-title":"Mater Eval"},{"issue":"5","key":"827_CR8","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3390\/agriculture10050178","volume":"10","author":"Y Li","year":"2020","unstructured":"Li, Y., Chao, X.: ANN-based continual classification in agriculture. Agriculture 10(5), 178 (2020)","journal-title":"Agriculture"},{"key":"827_CR9","doi-asserted-by":"crossref","unstructured":"Liang X W, Jiang A P, Li T, et al. LR-SMOTE\u2013An improved unbalanced data set oversampling based on K-means and SVM. Knowledge-Based Systems, 2020: 105845.","DOI":"10.1016\/j.knosys.2020.105845"},{"key":"827_CR10","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ins.2018.10.029","volume":"477","author":"CF Tsai","year":"2019","unstructured":"Tsai, C.F., Lin, W.C., Hu, Y.H., et al.: Under-sampling class imbalanced datasets by combining clustering analysis and instance selection. Inf Sci 477, 47\u201354 (2019)","journal-title":"Inf Sci"},{"key":"827_CR11","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409","author":"WC Lin","year":"2017","unstructured":"Lin, W.C., Tsai, C.F., Hu, Y.H., et al.: Clustering-based undersampling in class-imbalanced data. Inf Sci 409, 17\u201326 (2017)","journal-title":"Inf Sci"},{"key":"827_CR12","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.eswa.2017.03.073","volume":"82","author":"G Douzas","year":"2017","unstructured":"Douzas, G., Bacao, F.: Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning. Expert Syst Appl 82, 40\u201352 (2017)","journal-title":"Expert Syst Appl"},{"key":"827_CR13","doi-asserted-by":"crossref","unstructured":"Gan D, Shen J, An B, et al. Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis. Comput Industrial Eng. 2020: 106266.","DOI":"10.1016\/j.cie.2019.106266"},{"key":"827_CR14","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.knosys.2016.09.032","volume":"115","author":"Q Fan","year":"2017","unstructured":"Fan, Q., Wang, Z., Li, D., et al.: Entropy-based fuzzy support vector machine for imbalanced datasets. Knowl Based Syst 115, 87\u201399 (2017)","journal-title":"Knowl Based Syst"},{"key":"827_CR15","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.patcog.2017.06.019","volume":"71","author":"B Tang","year":"2017","unstructured":"Tang, B., He, H.: GIR-based ensemble sampling approaches for imbalanced learning. Pattern Recogn 71, 306\u2013319 (2017)","journal-title":"Pattern Recogn"},{"issue":"2","key":"827_CR16","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1007\/s11063-018-09977-1","volume":"50","author":"YS Aurelio","year":"2019","unstructured":"Aurelio, Y.S., de Almeida, G.M., de Castro, C.L., et al.: Learning from imbalanced data sets with weighted cross-entropy function[J]. Neural Process Lett 50(2), 1937\u20131949 (2019)","journal-title":"Neural Process Lett"},{"key":"827_CR17","doi-asserted-by":"crossref","unstructured":"Li M, Xiong A, Wang L, et al. Aco Resampling: Enhancing the performance of oversampling methods for class imbalance classification. Knowledge-Based Systems, 2020: 105818.","DOI":"10.1016\/j.knosys.2020.105818"},{"key":"827_CR18","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.neucom.2018.04.089","volume":"343","author":"M Koziarski","year":"2019","unstructured":"Koziarski, M., Krawczyk, B., Wo\u017aniak, M.: Radial-Based oversampling for noisy imbalanced data classification. Neurocomputing 343, 19\u201333 (2019)","journal-title":"Neurocomputing"},{"key":"827_CR19","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.knosys.2018.12.021","volume":"166","author":"T Zhu","year":"2019","unstructured":"Zhu, T., Lin, Y., Liu, Y., et al.: Minority oversampling for imbalanced ordinal regression. Knowl Based Syst 166, 140\u2013155 (2019)","journal-title":"Knowl Based Syst"},{"key":"827_CR20","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ins.2019.07.070","volume":"505","author":"D Elreedy","year":"2019","unstructured":"Elreedy, D., Atiya, A.F.: A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Inf Sci 505, 32\u201364 (2019)","journal-title":"Inf Sci"},{"key":"827_CR21","unstructured":"Yang J, Zhao Y, Liu J, et al. No Reference Quality Assessment for Screen Content Images Using Stacked Autoencoders in Pictorial and Textual Regions. IEEE Transactions on Cybernetics, 2020."},{"issue":"3","key":"827_CR22","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1109\/TII.2020.2998818","volume":"17","author":"J Yang","year":"2020","unstructured":"Yang, J., Wang, C., Jiang, B., et al.: Visual perception enabled industry intelligence: state of the art, challenges and prospects. IEEE Trans Industr Inf 17(3), 2204\u20132219 (2020)","journal-title":"IEEE Trans Industr Inf"},{"issue":"5","key":"827_CR23","doi-asserted-by":"publisher","first-page":"4238","DOI":"10.1109\/JIOT.2019.2946269","volume":"7","author":"J Yang","year":"2019","unstructured":"Yang, J., Wen, J., Wang, Y., et al.: Fog-based marine environmental information monitoring toward ocean of things. IEEE Internet Things J 7(5), 4238\u20134247 (2019)","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"827_CR24","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MNET.011.1900374","volume":"34","author":"J Yang","year":"2020","unstructured":"Yang, J., Wen, J., Jiang, B., et al.: Blockchain-based sharing and tamper-proof framework of big data networking. IEEE Network 34(4), 62\u201367 (2020)","journal-title":"IEEE Network"},{"issue":"6","key":"827_CR25","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/s00530-020-00670-9","volume":"26","author":"H Shen","year":"2020","unstructured":"Shen, H., Lin, D., Song, T., et al.: Anti-distractors: two-branch siamese tracker with both static and dynamic filters for object tracking. Multimedia Syst 26(6), 631\u2013641 (2020)","journal-title":"Multimedia Syst"},{"issue":"6","key":"827_CR26","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1007\/s00530-020-00683-4","volume":"26","author":"M Fang","year":"2020","unstructured":"Fang, M., Bai, X., Zhao, J., et al.: Integrating Gaussian mixture model and dilated residual network for action recognition in videos. Multimedia Syst 26(6), 715\u2013725 (2020)","journal-title":"Multimedia Syst"},{"key":"827_CR27","doi-asserted-by":"crossref","unstructured":"Li Y, Yang J. Meta-learning baselines and database for few-shot classification in agriculture[J]. Computers and Electronics in Agriculture, 2021, 182: 106055.","DOI":"10.1016\/j.compag.2021.106055"},{"key":"827_CR28","doi-asserted-by":"crossref","unstructured":"Peng Z, Li Z, Zhang J, et al. Few-shot image recognition with knowledge transfer[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2019: 441\u2013449.","DOI":"10.1109\/ICCV.2019.00053"},{"key":"827_CR29","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, et al. Learning to compare: Relation network for few-shot learning[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 1199\u20131208.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"827_CR30","unstructured":"Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks[C]\/\/International Conference on Machine Learning. PMLR, 2017: 1126\u20131135."},{"key":"827_CR31","doi-asserted-by":"publisher","first-page":"105803","DOI":"10.1016\/j.compag.2020.105803","volume":"178","author":"Y Li","year":"2020","unstructured":"Li, Y., Nie, J., Chao, X.: Do we really need deep CNN for plant diseases identification? Comput Electron Agriculture 178, 105803 (2020)","journal-title":"Comput Electron Agriculture"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00827-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-021-00827-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00827-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T13:02:26Z","timestamp":1694782946000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-021-00827-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,3]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["827"],"URL":"https:\/\/doi.org\/10.1007\/s00530-021-00827-0","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,3]]},"assertion":[{"value":"1 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}