{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T14:20:22Z","timestamp":1781878822782,"version":"3.54.5"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,1,11]],"date-time":"2020-01-11T00:00:00Z","timestamp":1578700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,11]],"date-time":"2020-01-11T00:00:00Z","timestamp":1578700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["No.2015JM6347"],"award-info":[{"award-number":["No.2015JM6347"]}]},{"name":"Science Research Plan of Shangluo University","award":["No.14SKY026"],"award-info":[{"award-number":["No.14SKY026"]}]},{"name":"Horizontal Project of Shangluo University","award":["No.2018HXKY056"],"award-info":[{"award-number":["No.2018HXKY056"]}]},{"name":"Horizontal Project of Shangluo University","award":["19HKY082"],"award-info":[{"award-number":["19HKY082"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s11063-020-10191-1","type":"journal-article","created":{"date-parts":[[2020,1,11]],"date-time":"2020-01-11T07:02:50Z","timestamp":1578726170000},"page":"2603-2616","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Safe Semi-supervised Classification Algorithm Using Multiple Classifiers Ensemble"],"prefix":"10.1007","volume":"53","author":[{"given":"Jianhua","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,11]]},"reference":[{"issue":"3","key":"10191_CR1","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.ipm.2008.11.002","volume":"45","author":"M Li","year":"2009","unstructured":"Li M, Li H, Zhou ZH (2009) Semi-supervised document retrieval. Inf Process Manage 45(3):341\u2013355","journal-title":"Inf Process Manage"},{"issue":"1","key":"10191_CR2","first-page":"1","volume":"49","author":"NFFD Silva","year":"2016","unstructured":"Silva NFFD, Coletta LFS, Hruschka ER (2016) A Survey and comparative study of tweet sentiment analysis via semi-supervised learning. ACM Comput Surv 49(1):1\u201326","journal-title":"ACM Comput Surv"},{"issue":"2","key":"10191_CR3","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/LGRS.2008.2009077","volume":"6","author":"G Camps-Valls","year":"2009","unstructured":"Camps-Valls G, Munoz-Mari J, Gomez-Chova L et al (2009) Biophysical parameter estimation with a semisupervised support vector machine. IEEE Geosci Remote Sens Lett 6(2):248\u2013252","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"10191_CR4","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/TCYB.2015.2399456","volume":"46","author":"F Dornaika","year":"2015","unstructured":"Dornaika F, El Traboulsi Y, Dornaika F, El TY (2015) Learning flexible graph-based semi-supervised embedding. IEEE Trans Cybern 46(1):206\u2013218","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"10191_CR5","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TCSVT.2015.2400779","volume":"26","author":"Y Peng","year":"2016","unstructured":"Peng Y, Zhai X, Zhao Y et al (2016) Semi-supervised cross-media feature learning with unified patch graph regularization. IEEE Trans Circuits Syst Video Technol 26(3):583\u2013596","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"2","key":"10191_CR6","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1109\/TIE.2017.2726961","volume":"65","author":"TS Abdelgayed","year":"2018","unstructured":"Abdelgayed TS, Morsi WG, Sidhu TS (2018) Fault detection and classification based on co-training of semi-supervised machine learning. IEEE Trans Ind Electron 65(2):1595\u20131605","journal-title":"IEEE Trans Ind Electron"},{"issue":"3","key":"10191_CR7","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10115-009-0209-z","volume":"24","author":"ZH Zhou","year":"2010","unstructured":"Zhou ZH, Li M (2010) Semi-supervised learning by disagreement. Knowl Inf Syst 24(3):415\u2013439","journal-title":"Knowl Inf Syst"},{"key":"10191_CR8","doi-asserted-by":"crossref","unstructured":"Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on computational learning theory (COLT\u201998), pp 92\u2013100. ACM, Wisconsin","DOI":"10.1145\/279943.279962"},{"issue":"2","key":"10191_CR9","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/TCYB.2017.2761908","volume":"49","author":"ZW Yu","year":"2019","unstructured":"Yu ZW, Zhang YD, You JN et al (2019) Adaptive semi-supervised classifier ensemble for high dimensional data classification. IEEE Trans Cybern 49(2):366\u2013379","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"10191_CR10","doi-asserted-by":"crossref","first-page":"367","DOI":"10.3233\/IDA-130584","volume":"17","author":"MR Keyvanpour","year":"2013","unstructured":"Keyvanpour MR, Imani MB (2013) Semi-supervised text categorization: exploiting unlabeled data using ensemble learning algorithms. Intell Data Anal 17(3):367\u2013385","journal-title":"Intell Data Anal"},{"issue":"5","key":"10191_CR11","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1016\/j.asoc.2011.12.019","volume":"12","author":"GX Yu","year":"2012","unstructured":"Yu GX, Zhang GJ, Yu ZW et al (2012) Semi-supervised ensemble classification in subspaces. Appl Soft Comput 12(5):1511\u20131522","journal-title":"Appl Soft Comput"},{"issue":"11","key":"10191_CR12","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TKDE.2005.186","volume":"17","author":"ZH Zhou","year":"2005","unstructured":"Zhou ZH, Li M (2005) Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans Knowl Data Eng 17(11):1529\u20131541","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10191_CR13","first-page":"73","volume":"35","author":"Y Li","year":"2012","unstructured":"Li Y, Guo M (2012) A new relational Tri-training system with adaptive data editing for inductive logic programming. Knowl-Based Syst 35:73\u2013185","journal-title":"Knowl-Based Syst"},{"key":"10191_CR14","first-page":"669","volume":"4","author":"YF Li","year":"2019","unstructured":"Li YF, Liang DM (2019) Safe semi-supervised learning: a brief introduction. Front Comput Sci 4:669\u2013676","journal-title":"Front Comput Sci"},{"issue":"1","key":"10191_CR15","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TPAMI.2014.2299812","volume":"37","author":"YF Li","year":"2015","unstructured":"Li YF, Zhou ZH (2015) Towards making unlabeled data never hurt. IEEE Trans Pattern Anal Mach Intell 37(1):175\u2013188","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10191_CR16","unstructured":"Li YF, Zhou ZH (2011) Improving semi-supervised support vector machines through unlabeled instances selection. In: Proceedings of the 25th AAAI conference on artificial intelligence, pp 386\u2013391"},{"key":"10191_CR17","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1007\/s13042-018-0788-7","volume":"10","author":"N Sang","year":"2019","unstructured":"Sang N, Gan H, Fan Y et al (2019) Adaptive safety degree-based safe semi-supervised learning. Int J Mach Learn Cybernet 10:1101\u20131108","journal-title":"Int J Mach Learn Cybernet"},{"key":"10191_CR18","unstructured":"Goldman S, Zhou Y (2000) Enhancing supervised learning with unlabeled data. In: Proceedings of the 17th international conference on machine learning, pp 327\u2013334"},{"issue":"1\u20132","key":"10191_CR19","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/int.10157","volume":"19","author":"N Soonthornphisaj","year":"2004","unstructured":"Soonthornphisaj N, Kijsirikul B (2004) Interative cross-training: an algorithm for learning from unlabeled Web pages. Int J Intell Syst 19(1\u20132):131\u2013147","journal-title":"Int J Intell Syst"},{"issue":"11","key":"10191_CR20","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1109\/TPAMI.2008.235","volume":"31","author":"PK Mallapragada","year":"2009","unstructured":"Mallapragada PK, Jin R, Jain AK et al (2009) SemiBoost: boosting for semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 31(11):2000\u20132014","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"10191_CR21","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TNNLS.2016.2637881","volume":"29","author":"J Peng","year":"2018","unstructured":"Peng J, Aved AJ, Seetharaman G et al (2018) Multiview boosting with information propagation for classification. IEEE Trans Neural Netw Learn Syst 29(3):657\u2013669","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"8","key":"10191_CR22","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1162\/089976600300015178","volume":"12","author":"H Schwenk","year":"2000","unstructured":"Schwenk H, Bengio Y (2000) Boosting neural networks. Neural Comput 12(8):1869\u20131887","journal-title":"Neural Comput"},{"issue":"12","key":"10191_CR23","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.1109\/TPAMI.2010.47","volume":"32","author":"C Shen","year":"2009","unstructured":"Shen C, Li H (2009) On the dual formulation of boosting algorithms. IEEE Trans Pattern Anal Mach Intell 32(12):2216\u20132231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"10191_CR24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.knosys.2013.02.009","volume":"45","author":"E Rashedi","year":"2013","unstructured":"Rashedi E, Mirzaei A (2013) A hierarchical cluster ensemble method based on boosting theory. Knowl-Based Syst 45(3):83\u201393","journal-title":"Knowl-Based Syst"},{"key":"10191_CR25","first-page":"544","volume":"20","author":"J Li","year":"2019","unstructured":"Li J, Zhang L, Feng X, Jia K, Kong F (2019) Feature extraction and area identification of wireless channel in mobile communication. J Int Technol 20:544\u2013553","journal-title":"J Int Technol"},{"key":"10191_CR26","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2112\/SI73-006.1","volume":"73","author":"C Mi","year":"2015","unstructured":"Mi C, Shen Y, Mi WJ, Huang YF (2015) Ship identification algorithm based on 3D point cloud for automated ship loaders. J Coast Res 73:28\u201334","journal-title":"J Coast Res"},{"key":"10191_CR27","doi-asserted-by":"crossref","first-page":"63976","DOI":"10.1109\/ACCESS.2018.2877428","volume":"6","author":"A Yang","year":"2018","unstructured":"Yang A, Li S, Ren C, Liu H, Han Y, Liu L (2018) Situational awareness system in the smart campus. IEEE Access 6:63976\u201363986","journal-title":"IEEE Access"},{"key":"10191_CR28","doi-asserted-by":"crossref","first-page":"50187","DOI":"10.1109\/ACCESS.2018.2868951","volume":"6","author":"A Yang","year":"2018","unstructured":"Yang A, Li Y, Kong F, Wang G, Chen E (2018) security control redundancy allocation technology and security keys based on internet of things. IEEE Access. 6:50187\u201350196","journal-title":"IEEE Access."},{"key":"10191_CR29","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MIM.2018.8278808","volume":"21","author":"Y Yang","year":"2018","unstructured":"Yang Y, Zhong M, Yao H, Yu F, Fu X, Postolache O (2018) Internet of things for smart ports: technologies and challenges. IEEE Instrum Meas Mag 21:34\u201343","journal-title":"IEEE Instrum Meas Mag"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10191-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-020-10191-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10191-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T12:07:10Z","timestamp":1626869230000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-020-10191-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,11]]},"references-count":29,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["10191"],"URL":"https:\/\/doi.org\/10.1007\/s11063-020-10191-1","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,11]]},"assertion":[{"value":"11 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}