{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T10:13:22Z","timestamp":1780568002738,"version":"3.54.1"},"reference-count":121,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T00:00:00Z","timestamp":1556150400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s11042-019-7543-2","type":"journal-article","created":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T11:04:58Z","timestamp":1556276698000},"page":"33375-33400","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A review of improved extreme learning machine methods for data stream classification"],"prefix":"10.1007","volume":"78","author":[{"given":"Li","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizhi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saihua","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,4,25]]},"reference":[{"key":"7543_CR1","doi-asserted-by":"crossref","unstructured":"AI-Behadili H, Grumpet A, Doppt C et al (2015) Extreme Learning Machine based Novelty Detection for Incremental Semi-Supervised Learning. In 2015 Third International Conference on Image Infonnation Processing. IEEE","DOI":"10.1109\/ICIIP.2015.7414771"},{"key":"7543_CR2","doi-asserted-by":"crossref","unstructured":"Alade OA, Selamat A, Sallehuddin R (2017) A review of advances in extreme learning machine techniques and its applications. In International Conference of Reliable Information and Communication Technology:885\u2013895. Springer","DOI":"10.1007\/978-3-319-59427-9_91"},{"key":"7543_CR3","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jocs.2017.03.006","volume":"25","author":"S Aljawarneh","year":"2018","unstructured":"Aljawarneh S, Aldwairi M, Yassein MB (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science 25:152\u2013160. \nhttps:\/\/doi.org\/10.1016\/j.jocs.2017.03.006","journal-title":"Journal of Computational Science"},{"issue":"5","key":"7543_CR4","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1007\/s12559-018-9564-y","volume":"10","author":"Buse Gul Atli","year":"2018","unstructured":"Atli BG, Miche Y, Kalliola A (2018) Anomaly-based intrusion detection using extreme learning machine and aggregation of network traffic statistics in probability space. Cogn Comput. \nhttps:\/\/doi.org\/10.1007\/s12559-018-9564-y","journal-title":"Cognitive Computation"},{"key":"7543_CR5","doi-asserted-by":"crossref","unstructured":"Bloodgood M, Vijay-Shanker K (2009) Taking into account the differences between actively and passively acquired data: The case of active learning with support vector machines for imbalanced datasets. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers. Association for Computational Linguistics, pp 137-140","DOI":"10.3115\/1620853.1620892"},{"issue":"Sep","key":"7543_CR6","first-page":"1579","volume":"6","author":"A Bordes","year":"2005","unstructured":"Bordes A, Ertekin S, Weston J et al (2005) Fast kernel classifiers with online and active learning. J Mach Learn Res 6(Sep):1579\u20131619","journal-title":"J Mach Learn Res"},{"key":"7543_CR7","unstructured":"Cao J, Lin Z, Huang G-B (2013) Voting base online sequential extreme learning machine for multi-class classification. In Circuits and Systems (ISCAS), 2013 IEEE International Symposium on:2327-2330. IEEE"},{"issue":"1","key":"7543_CR8","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/s12559-014-9279-7","volume":"7","author":"K Cao","year":"2014","unstructured":"Cao K, Wang G, Han D et al (2014) Classification of uncertain data streams based on extreme learning machine. Cogn Comput 7(1):150\u2013160. \nhttps:\/\/doi.org\/10.1007\/s12559-014-9279-7","journal-title":"Cogn Comput"},{"issue":"10","key":"7543_CR9","doi-asserted-by":"publisher","first-page":"4528","DOI":"10.1016\/j.jfranklin.2015.07.002","volume":"352","author":"J Cao","year":"2015","unstructured":"Cao J, Zhao Y, Lai X et al (2015) Landmark recognition with sparse representation classification and extreme learning machine. Journal of the Franklin Institute 352(10):4528\u20134545. \nhttps:\/\/doi.org\/10.1016\/j.jfranklin.2015.07.002","journal-title":"Journal of the Franklin Institute"},{"key":"7543_CR10","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.neucom.2015.05.121","volume":"174","author":"K Cao","year":"2016","unstructured":"Cao K, Wang G, Han D et al (2016) An algorithm for classification over uncertain data based on extreme learning machine. Neurocomputing 174:194\u2013202. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2015.05.121","journal-title":"Neurocomputing"},{"key":"7543_CR11","doi-asserted-by":"crossref","unstructured":"Cavalcante RC, I. Oliveira AL (2015) An Approach to Handle Concept Drift in Financial Time Series Based on Extreme Learning Machines and Explicit Drift Detection. In 2015 International Joint Conference on Neural Networks. IEEE","DOI":"10.1109\/IJCNN.2015.7280721"},{"key":"7543_CR12","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1109\/TPAMI.2003.1233909","volume":"25","author":"R Cucchiara","year":"2003","unstructured":"Cucchiara R, Piccard M, Prati A (2003) Detecting moving objects, ghosts and shadows in Vedio streams. IEEE Transaction on Pattern Analysis And Machine Intelligence 25:1337\u20131342. \nhttps:\/\/doi.org\/10.1109\/TPAMI.2003.1233909","journal-title":"IEEE Transaction on Pattern Analysis And Machine Intelligence"},{"key":"7543_CR13","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.neucom.2015.06.087","volume":"174","author":"W-Y Deng","year":"2016","unstructured":"Deng W-Y, Ong Y-S, Tan PS et al (2016) Online sequential reduced kernel extreme learning machine. Neurocomputing 174:72\u201384. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2015.06.087","journal-title":"Neurocomputing"},{"key":"7543_CR14","doi-asserted-by":"publisher","unstructured":"Deng S, Wang B, Huang S et al (2017) Self-adaptive framework for efficient stream data classification on storm. IEEE Transactions on Systems, Man, and Cybernetics: Systems:1\u201314. \nhttps:\/\/doi.org\/10.1109\/tsmc.2017.2757029","DOI":"10.1109\/tsmc.2017.2757029"},{"issue":"2","key":"7543_CR15","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/s10661-016-5094-9","volume":"188","author":"RC Deo","year":"2016","unstructured":"Deo RC, Sahin M (2016) An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environ Monit Assess 188(2):90. \nhttps:\/\/doi.org\/10.1007\/s10661-016-5094-9","journal-title":"Environ Monit Assess"},{"issue":"1","key":"7543_CR16","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s10462-013-9405-z","volume":"44","author":"S Ding","year":"2013","unstructured":"Ding S, Zhao H, Zhang Y et al (2013) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44(1):103\u2013115. \nhttps:\/\/doi.org\/10.1007\/s10462-013-9405-z","journal-title":"Artif Intell Rev"},{"key":"7543_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/129021","volume":"2015","author":"S Ding","year":"2015","unstructured":"Ding S, Zhang N, Xu X et al (2015) Deep extreme learning machine and its application in EEG classification. Math Probl Eng 2015:1\u201311. \nhttps:\/\/doi.org\/10.1155\/2015\/129021","journal-title":"Math Probl Eng"},{"key":"7543_CR18","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.neucom.2017.02.102","volume":"277","author":"S Ding","year":"2018","unstructured":"Ding S, Mirza B, Lin Z et al (2018) Kernel based online learning for imbalance multiclass classification. Neurocomputing 277:139\u2013148","journal-title":"Neurocomputing"},{"issue":"10","key":"7543_CR19","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1109\/tkde.2012.136","volume":"25","author":"G Ditzler","year":"2013","unstructured":"Ditzler G, Polikar R (2013) Incremental learning of concept drift from streaming imbalanced data. IEEE Trans Knowl Data Eng 25(10):2283\u20132301. \nhttps:\/\/doi.org\/10.1109\/tkde.2012.136","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7543_CR20","doi-asserted-by":"crossref","unstructured":"Domingos P, Hulten G (2000) Mining high-speed data streams. In 6th ACM SIGKDD international conference on Knowledge discovery and data mining, 71-80. ACM","DOI":"10.1145\/347090.347107"},{"key":"7543_CR21","doi-asserted-by":"crossref","unstructured":"B. Dongre P, Malik. LG (2014a) A review on Real Time Data Stream Classification and Adapting To Various Concept Drift Scenarios. In 2014 IEEE International Advance Computing Conference . IEEE","DOI":"10.1109\/IAdCC.2014.6779381"},{"key":"7543_CR22","doi-asserted-by":"crossref","unstructured":"Dongre PB, Malik LG (2014b) A review on real time data stream classification and adapting to various concept drift scenarios. In Advance Computing Conference (IACC), 2014 IEEE International:533-537. IEEE","DOI":"10.1109\/IAdCC.2014.6779381"},{"key":"7543_CR23","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-319-28397-5_28","volume-title":"Proceedings of ELM-2015 Volume 1","author":"Lijuan Duan","year":"2016","unstructured":"Duan L, Xu Y, Cui S (2016) Feature Extraction of Motor Imagery EEG Based on Extreme Learning Machine Auto-encoder. In: Proceedings in Adaptation, Learning and Optimization, vol 6. pp 361-370. doi:\nhttps:\/\/doi.org\/10.1007\/978-3-319-28397-5_28"},{"issue":"6","key":"7543_CR24","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/s12559-017-9494-0","volume":"9","author":"L Duan","year":"2017","unstructured":"Duan L, Bao M, Cui S (2017) Motor imagery EEG classification based on kernel hierarchical extreme learning machine. Cogn Comput 9(6):758\u2013765. \nhttps:\/\/doi.org\/10.1007\/s12559-017-9494-0","journal-title":"Cogn Comput"},{"key":"7543_CR25","doi-asserted-by":"crossref","unstructured":"Eirola E, Gritsenko A, Akusok A (2015) Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models. In International Work-Conference on Artificial Neural Networks:153-164. Springer","DOI":"10.1007\/978-3-319-19222-2_13"},{"key":"7543_CR26","unstructured":"Fan W, Wang H, Philip SY et al (2003) Is random model better? On its accuracy and efficiency. In Third IEEE International Conference on Data Mining:51. IEEE"},{"key":"7543_CR27","doi-asserted-by":"crossref","unstructured":"Gaber MM, Zaslavsky A, Krishnaswamy S (2007) A survey of classification methods in data streams. In: Data streams. Springer, pp 39-59","DOI":"10.1007\/978-0-387-47534-9_3"},{"issue":"6","key":"7543_CR28","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/MIC.2008.119","volume":"12","author":"Jing Gao","year":"2008","unstructured":"Gao J, Ding B, Fan W et al (2008) Classifying data streams with skewed class distributions and concept drifts. IEEE Internet Comput 12(6)","journal-title":"IEEE Internet Computing"},{"key":"7543_CR29","unstructured":"Geoff H, Laurie S, Pedro D (2001) MiningTime-ChangingDataStreams.In 7th ACM SIGKDD international conference on Knowledge discovery and data mining:97-106. ACM"},{"key":"7543_CR30","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.neucom.2013.05.003","volume":"122","author":"A Ghazikhani","year":"2013","unstructured":"Ghazikhani A, Monsefi R, Yazdi HS (2013) Ensemble of online neural networks for non-stationary and imbalanced data streams. Neurocomputing 122:535\u2013544","journal-title":"Neurocomputing"},{"issue":"2","key":"7543_CR31","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1145\/3054925","volume":"50","author":"HM Gomes","year":"2017","unstructured":"Gomes HM, Barddal JP, Enembreck F et al (2017) A survey on ensemble learning for data stream classification. ACM Computing Surveys (CSUR) 50(2):23","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"7543_CR32","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neucom.2013.02.047","volume":"128","author":"Y Gu","year":"2014","unstructured":"Gu Y, Liu J, Chen Y et al (2014) TOSELM: timeliness online sequential extreme learning machine. Neurocomputing 128:119\u2013127. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2013.02.047","journal-title":"Neurocomputing"},{"key":"7543_CR33","doi-asserted-by":"publisher","unstructured":"Guang-Bin H, Qin-Yu Z, Chee-Kheong S (2004a) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks. pp 985-990. doi:\nhttps:\/\/doi.org\/10.1109\/ijcnn.2004.1380068","DOI":"10.1109\/ijcnn.2004.1380068"},{"key":"7543_CR34","unstructured":"Guang-Bin H, Qin-Yu Z, Chee-Kheong S (2004b) Extreme learning machine: a new learning scheme of feedforward neural networks. In 2004 IEEE International Joint Conference on Neural Networks 2:985-990. IEEE"},{"key":"7543_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/6195387","volume":"2018","author":"W Guo","year":"2018","unstructured":"Guo W, Xu T, Tang K et al (2018) Online sequential extreme learning machine with generalized regularization and adaptive forgetting factor for time-varying system prediction. Math Probl Eng 2018:1\u201322. \nhttps:\/\/doi.org\/10.1155\/2018\/6195387","journal-title":"Math Probl Eng"},{"issue":"4","key":"7543_CR36","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1007\/s11390-015-1566-6","volume":"30","author":"D-H Han","year":"2015","unstructured":"Han D-H, Zhang X, Wang G-R (2015a) Classifying uncertain and evolving data streams with distributed extreme learning machine. J Comput Sci Technol 30(4):874\u2013887. \nhttps:\/\/doi.org\/10.1007\/s11390-015-1566-6","journal-title":"J Comput Sci Technol"},{"issue":"4","key":"7543_CR37","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s10489-015-0675-9","volume":"43","author":"D Han","year":"2015","unstructured":"Han D, Giraud-Carrier C, Li S (2015b) Efficient mining of high-speed uncertain data streams. Appl Intell 43(4):773\u2013785. \nhttps:\/\/doi.org\/10.1007\/s10489-015-0675-9","journal-title":"Appl Intell"},{"key":"7543_CR38","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.neucom.2016.09.092","volume":"228","author":"F Han","year":"2017","unstructured":"Han F, Zhao M-R, Zhang J-M et al (2017) An improved incremental constructive single-hidden-layer feedforward networks for extreme learning machine based on particle swarm optimization. Neurocomputing 228:133\u2013142. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2016.09.092","journal-title":"Neurocomputing"},{"key":"7543_CR39","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.neucom.2017.03.094","volume":"277","author":"D Han","year":"2018","unstructured":"Han D, Li S, Wei F et al (2018) Two birds with one stone: classifying positive and unlabeled examples on uncertain data streams. Neurocomputing 277:149\u2013160. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2017.03.094","journal-title":"Neurocomputing"},{"issue":"1","key":"7543_CR40","doi-asserted-by":"publisher","first-page":"8","DOI":"10.14419\/jacst.v5i1.5225","volume":"5","author":"S Homayoun","year":"2016","unstructured":"Homayoun S, Ahmadzadeh M (2016) A review on data stream classification approaches. Journal of Advanced Computer Science & Technology 5(1):8\u201313","journal-title":"Journal of Advanced Computer Science & Technology"},{"issue":"1","key":"7543_CR41","doi-asserted-by":"publisher","first-page":"333","DOI":"10.14257\/ijunesst.2015.8.1.30","volume":"8","author":"Y Huang","year":"2015","unstructured":"Huang Y (2015) Dynamic cost-sensitive ensemble classification based on extreme learning machine for mining imbalanced massive data streams. International Journal of u- and e-Service, Science and Technology 8(1):333\u2013346. \nhttps:\/\/doi.org\/10.14257\/ijunesst.2015.8.1.30","journal-title":"International Journal of u- and e-Service, Science and Technology"},{"issue":"16-18","key":"7543_CR42","doi-asserted-by":"publisher","first-page":"3460","DOI":"10.1016\/j.neucom.2007.10.008","volume":"71","author":"G-B Huang","year":"2008","unstructured":"Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16-18):3460\u20133468. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2007.10.008","journal-title":"Neurocomputing"},{"issue":"6","key":"7543_CR43","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1109\/tsmcb.2004.834428","volume":"34","author":"GB Huang","year":"2004","unstructured":"Huang GB, Saratchandran P, Sundararajan N (2004) An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34(6):2284\u20132292. \nhttps:\/\/doi.org\/10.1109\/tsmcb.2004.834428","journal-title":"IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)"},{"issue":"1","key":"7543_CR44","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/TNN.2004.836241","volume":"16","author":"GB Huang","year":"2005","unstructured":"Huang GB, Saratchandran P, Sundararajan N (2005) A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation. IEEE Trans Neural Netw 16(1):57\u201367. \nhttps:\/\/doi.org\/10.1109\/TNN.2004.836241","journal-title":"IEEE Trans Neural Netw"},{"issue":"1-3","key":"7543_CR45","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1-3):489\u2013501. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2005.12.126","journal-title":"Neurocomputing"},{"issue":"2","key":"7543_CR46","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G-B Huang","year":"2012","unstructured":"Huang G-B, Zhou H, Ding X et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42(2):513\u2013529","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"key":"7543_CR47","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","volume":"61","author":"G Huang","year":"2015","unstructured":"Huang G, Huang GB, Song S et al (2015) Trends in extreme learning machines: a review. Neural Netw 61:32\u201348. \nhttps:\/\/doi.org\/10.1016\/j.neunet.2014.10.001","journal-title":"Neural Netw"},{"key":"7543_CR48","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1016\/j.procs.2017.11.440","volume":"122","author":"MS Janardan","year":"2017","unstructured":"Janardan MS (2017) Concept drift in streaming data classification :Algorithms,Platforms and issues. Procedia Computer Science 122:804\u2013811. \nhttps:\/\/doi.org\/10.1016\/j.procs.2017.11.440","journal-title":"Procedia Computer Science"},{"key":"7543_CR49","doi-asserted-by":"crossref","unstructured":"Junhai Z, Jinggeng W, Xizhao W (2014) Ensemble online sequential extreme learning machine for large data set classification In 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE","DOI":"10.1109\/SMC.2014.6974260"},{"key":"7543_CR50","unstructured":"Kasun LLC, Zhou H, Huang G-B (2013) Representational learning with extreme learning with extreme learning machine for big data. IEEE Intell Syst"},{"key":"7543_CR51","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"WP Kegelmeyer","year":"2002","unstructured":"Kegelmeyer WP, Hall LO, Bowyer KW et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357. \nhttps:\/\/doi.org\/10.1613\/jair.953","journal-title":"J Artif Intell Res"},{"key":"7543_CR52","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2016.01.009","volume":"191","author":"I Khan","year":"2016","unstructured":"Khan I, Huang JZ, Ivanov K (2016) Incremental density-based ensemble clustering over evolving data streams. Neurocomputing 191:34\u201343. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2016.01.009","journal-title":"Neurocomputing"},{"key":"7543_CR53","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neucom.2011.12.048","volume":"102","author":"Y Kim","year":"2013","unstructured":"Kim Y, Toh K-A, Teoh ABJ et al (2013) An online learning network for biometric scores fusion. Neurocomputing 102:65\u201377","journal-title":"Neurocomputing"},{"key":"7543_CR54","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1016\/j.procs.2016.05.509","volume":"80","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B (2016a) GPU-accelerated extreme learning Machines for Imbalanced Data Streams with concept drift. Procedia Computer Science 80:1692\u20131701. \nhttps:\/\/doi.org\/10.1016\/j.procs.2016.05.509","journal-title":"Procedia Computer Science"},{"issue":"4","key":"7543_CR55","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B (2016b) Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence 5(4):221\u2013232. \nhttps:\/\/doi.org\/10.1007\/s13748-016-0094-0","journal-title":"Progress in Artificial Intelligence"},{"key":"7543_CR56","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.neucom.2014.10.025","volume":"150","author":"B Krawczyk","year":"2015","unstructured":"Krawczyk B, Stefanowski J, Wozniak M (2015) Data stream classification and big data analytics. Neurocomputing 150:238\u2013239. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2014.10.025","journal-title":"Neurocomputing"},{"issue":"4","key":"7543_CR57","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1007\/s10586-017-0985-2","volume":"20","author":"Y Kuang","year":"2017","unstructured":"Kuang Y, Wu Q, Shao J et al (2017) Extreme learning machine classification method for lower limb movement recognition. Clust Comput 20(4):3051\u20133059. \nhttps:\/\/doi.org\/10.1007\/s10586-017-0985-2","journal-title":"Clust Comput"},{"key":"7543_CR58","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.enbuild.2018.06.056","volume":"176","author":"S Kumar","year":"2018","unstructured":"Kumar S, Pal SK, Singh RP (2018) A novel method based on extreme learning machine to predict heating and cooling load through design and structural attributes. Energy and Buildings 176:275\u2013286. \nhttps:\/\/doi.org\/10.1016\/j.enbuild.2018.06.056","journal-title":"Energy and Buildings"},{"issue":"13-15","key":"7543_CR59","doi-asserted-by":"publisher","first-page":"3391","DOI":"10.1016\/j.neucom.2009.02.013","volume":"72","author":"Y Lan","year":"2009","unstructured":"Lan Y, Soh YC, Huang G-B (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72(13-15):3391\u20133395. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2009.02.013","journal-title":"Neurocomputing"},{"key":"7543_CR60","doi-asserted-by":"crossref","unstructured":"Li P, Wu X, Liang Q et al (2011) Random ensemble decision trees for learning concept-drifting data streams. In Pacific-Asia Conference on Knowledge Discovery and Data Mining:313-325. Springer","DOI":"10.1007\/978-3-642-20841-6_26"},{"key":"7543_CR61","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.neucom.2015.04.024","volume":"166","author":"P Li","year":"2015","unstructured":"Li P, Wu X, Hu X et al (2015) Learning concept-drifting data streams with random ensemble decision trees. Neurocomputing 166:68\u201383. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2015.04.024","journal-title":"Neurocomputing"},{"key":"7543_CR62","unstructured":"Li M, Xiao P, Zhang J (2018a) Text classification based on ensemble extreme learning machine. arXiv preprint arXiv:180506525"},{"issue":"2","key":"7543_CR63","doi-asserted-by":"publisher","first-page":"e0192216","DOI":"10.1371\/journal.pone.0192216","volume":"13","author":"Y Li","year":"2018","unstructured":"Li Y, Qiu R, Jing S (2018b) Intrusion detection system using online sequence extreme learning machine (OS-ELM) in advanced metering infrastructure of smart grid. PLoS One 13(2):e0192216. \nhttps:\/\/doi.org\/10.1371\/journal.pone.0192216","journal-title":"PLoS One"},{"issue":"6","key":"7543_CR64","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","volume":"17","author":"NY Liang","year":"2006","unstructured":"Liang NY, Huang GB, Saratchandran P et al (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411\u20131423. \nhttps:\/\/doi.org\/10.1109\/TNN.2006.880583","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"7543_CR65","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1023\/B:MACH.0000011805.60520.fe","volume":"54","author":"M Lindenbaum","year":"2004","unstructured":"Lindenbaum M, Markovitch S, Rusakov D (2004) Selective sampling for nearest neighbor classifiers. Mach Learn 54(2):125\u2013152","journal-title":"Mach Learn"},{"issue":"7","key":"7543_CR66","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1109\/LSP.2010.2053356","volume":"17","author":"N Liu","year":"2010","unstructured":"Liu N, Wang H (2010) Ensemble based extreme learning machine. IEEE Signal Processing Letters 17(7):754","journal-title":"IEEE Signal Processing Letters"},{"key":"7543_CR67","unstructured":"Lu Y, N S PS (1997) A sequential learning scheme for function approximation using minimal radial basis function neural networks. Neural Comput:461\u2013478"},{"issue":"3","key":"7543_CR68","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s12555-014-0309-8","volume":"13","author":"X Luo","year":"2015","unstructured":"Luo X, Chang X (2015) A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks. Int J Control Autom Syst 13(3):539\u2013546. \nhttps:\/\/doi.org\/10.1007\/s12555-014-0309-8","journal-title":"Int J Control Autom Syst"},{"key":"7543_CR69","unstructured":"Mao W, Wang J, Wang L (2015) Online Sequential Classification of Imbalanced Data by Combining Extreme Learning Machine and improved SMOTE Algorithm. In 2015 International Joint Conference on Neural Networks 1-8. IEEE"},{"key":"7543_CR70","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.neunet.2016.04.008","volume":"80","author":"B Mirza","year":"2016","unstructured":"Mirza B, Lin Z (2016) Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification. Neural Netw 80:79\u201394. \nhttps:\/\/doi.org\/10.1016\/j.neunet.2016.04.008","journal-title":"Neural Netw"},{"issue":"3","key":"7543_CR71","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/s11063-013-9286-9","volume":"38","author":"B Mirza","year":"2013","unstructured":"Mirza B, Lin Z, Toh K-A (2013) Weighted online sequential extreme learning machine for class imbalance learning. Neural Process Lett 38(3):465\u2013486. \nhttps:\/\/doi.org\/10.1007\/s11063-013-9286-9","journal-title":"Neural Process Lett"},{"key":"7543_CR72","doi-asserted-by":"publisher","unstructured":"Mirza B, Lin Z, Cao J et al (2015a) Voting based weighted online sequential extreme learning machine for imbalance multi-class classification. IEEE International Symposium on Circuits & Systems:565\u2013568. \nhttps:\/\/doi.org\/10.1109\/iscas.2015.7168696","DOI":"10.1109\/iscas.2015.7168696"},{"key":"7543_CR73","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.neucom.2014.03.075","volume":"149","author":"B Mirza","year":"2015","unstructured":"Mirza B, Lin Z, Liu N (2015b) Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing 149:316\u2013329","journal-title":"Neurocomputing"},{"key":"7543_CR74","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.compag.2015.08.008","volume":"117","author":"K Mohammadi","year":"2015","unstructured":"Mohammadi K, Shamshirband S, Motamedi S et al (2015) Extreme learning machine based prediction of daily dew point temperature. Comput Electron Agric 117:214\u2013225","journal-title":"Comput Electron Agric"},{"issue":"3","key":"7543_CR75","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s10115-014-0808-1","volume":"45","author":"H-L Nguyen","year":"2014","unstructured":"Nguyen H-L, Woon Y-K, Ng W-K (2014) A survey on data stream clustering and classification. Knowl Inf Syst 45(3):535\u2013569. \nhttps:\/\/doi.org\/10.1007\/s10115-014-0808-1","journal-title":"Knowl Inf Syst"},{"key":"7543_CR76","doi-asserted-by":"publisher","unstructured":"Ouyang Z, Zhou M, Wang T et al (2009) Mining Concept-Drifting and Noisy Data Streams Using Ensemble Classifiers.360-364. doi:\nhttps:\/\/doi.org\/10.1109\/aici.2009.153","DOI":"10.1109\/aici.2009.153"},{"key":"7543_CR77","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1007\/978-3-642-13657-3_52","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Shirui Pan","year":"2010","unstructured":"Pan S, Wu K, Zhang Y et al (2010a) Classifier Ensemble for Uncertain Data Stream Classification. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp 488-495. doi:\nhttps:\/\/doi.org\/10.1007\/978-3-642-13657-3_52"},{"key":"7543_CR78","doi-asserted-by":"crossref","unstructured":"Pan S, Wu K, Zhang Y et al (2010b) Classifier Ensemble for Uncertain Data Stream Classification. In Pacific-Asia Conference on Knowledge Discovery and Data Mining 6118:488-495. Springer","DOI":"10.1007\/978-3-642-13657-3_52"},{"issue":"2","key":"7543_CR79","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1162\/neco.1991.3.2.213","volume":"3","author":"J Platt","year":"1991","unstructured":"Platt J (1991) A resource-allocating network for function interpolation. Neural Comput 3(2):213\u2013225. \nhttps:\/\/doi.org\/10.1162\/neco.1991.3.2.213","journal-title":"Neural Comput"},{"issue":"11","key":"7543_CR80","doi-asserted-by":"publisher","first-page":"3817","DOI":"10.1016\/j.patcog.2010.05.028","volume":"43","author":"R Polikar","year":"2010","unstructured":"Polikar R, DePasquale J, Syed Mohammed H et al (2010) Learn++.MF: a random subspace approach for the missing feature problem. Pattern Recogn 43(11):3817\u20133832. \nhttps:\/\/doi.org\/10.1016\/j.patcog.2010.05.028","journal-title":"Pattern Recogn"},{"key":"7543_CR81","doi-asserted-by":"publisher","unstructured":"Qiu J, Wu Q, Ding G et al (2016) A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing 2016(1). \nhttps:\/\/doi.org\/10.1186\/s13634-016-0355-x","DOI":"10.1186\/s13634-016-0355-x"},{"key":"7543_CR82","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2017.01.078","volume":"239","author":"S Ram\u00edrez-Gallego","year":"2017","unstructured":"Ram\u00edrez-Gallego S, Krawczyk B, Garc\u00eda S et al (2017) A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing 239:39\u201357","journal-title":"Neurocomputing"},{"issue":"4","key":"7543_CR83","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1016\/j.jfranklin.2017.06.006","volume":"355","author":"S Roshan","year":"2018","unstructured":"Roshan S, Miche Y, Akusok A et al (2018) Adaptive and online network intrusion detection system using clustering and extreme learning machines. Journal of the Franklin Institute 355(4):1752\u20131779. \nhttps:\/\/doi.org\/10.1016\/j.jfranklin.2017.06.006","journal-title":"Journal of the Franklin Institute"},{"issue":"4","key":"7543_CR84","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1109\/jstars.2014.2301775","volume":"7","author":"A Samat","year":"2014","unstructured":"Samat A, Du P, Liu S et al (2014) ${{\\text E}^{2}}{\\text LMs}$: ensemble extreme learning Machines for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(4):1060\u20131069. \nhttps:\/\/doi.org\/10.1109\/jstars.2014.2301775","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"2","key":"7543_CR85","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s12559-013-9223-2","volume":"6","author":"R Savitha","year":"2013","unstructured":"Savitha R, Suresh S, Kim HJ (2013) A meta-cognitive learning algorithm for an extreme learning machine classifier. Cogn Comput 6(2):253\u2013263. \nhttps:\/\/doi.org\/10.1007\/s12559-013-9223-2","journal-title":"Cogn Comput"},{"issue":"9","key":"7543_CR86","doi-asserted-by":"publisher","first-page":"2214","DOI":"10.1109\/TNNLS.2014.2382094","volume":"26","author":"S Scardapane","year":"2015","unstructured":"Scardapane S, Comminiello D, Scarpiniti M et al (2015) Online sequential extreme learning machine with kernels. IEEE transactions on neural networks and learning systems 26(9):2214\u20132220","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"7543_CR87","doi-asserted-by":"crossref","unstructured":"Seliya N, Khoshgoftaar TM (2010) Active learning with neural networks for intrusion detection. In: Information Reuse and Integration (IRI), 2010 IEEE International Conference on. IEEE, pp 49-54","DOI":"10.1109\/IRI.2010.5558967"},{"issue":"5-6","key":"7543_CR88","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1007\/s00382-015-2682-2","volume":"46","author":"S Shamshirband","year":"2015","unstructured":"Shamshirband S, Mohammadi K, Tong CW (2015) Application of extreme learning machine for estimation of wind speed distribution. Clim Dyn 46(5-6):1893\u20131907. \nhttps:\/\/doi.org\/10.1007\/s00382-015-2682-2","journal-title":"Clim Dyn"},{"key":"7543_CR89","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1016\/j.neucom.2015.08.029","volume":"173","author":"Z Shao","year":"2016","unstructured":"Shao Z, Er MJ (2016) An online sequential learning algorithm for regularized extreme learning machine. Neurocomputing 173:778\u2013788. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2015.08.029","journal-title":"Neurocomputing"},{"key":"7543_CR90","doi-asserted-by":"crossref","unstructured":"Shukla S, Yadav R (2015) Voting based Extreme learning machine with entropy based ensemble pruning. In Cognitive Computing and Information Processing (CCIP), 2015 International Conference on:1-6. IEEE","DOI":"10.1109\/CCIP.2015.7100692"},{"key":"7543_CR91","doi-asserted-by":"crossref","unstructured":"Shukla A, Bhandari V, Shrivastava A (2017) Stay one Forget Multiple Extreme Learning Machine with deep Network using time interval process: A review. In 2017 7th International Conference on Communication Systems and Network Technologies (CSNT). IEEE","DOI":"10.1109\/CSNT.2017.8418548"},{"key":"7543_CR92","doi-asserted-by":"crossref","unstructured":"Sun J, Fu Y, Li S (2018) Sequential Human Activity Recognition based on Deep Convolutional Network and Extreme Learning Machine using Wearable Sensors","DOI":"10.1155\/2018\/8580959"},{"key":"7543_CR93","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.future.2017.03.026","volume":"75","author":"M Tennant","year":"2017","unstructured":"Tennant M, Stahl F, Rana O et al (2017) Scalable real-time classification of data streams with concept drift. Futur Gener Comput Syst 75:187\u2013199. \nhttps:\/\/doi.org\/10.1016\/j.future.2017.03.026","journal-title":"Futur Gener Comput Syst"},{"issue":"1","key":"7543_CR94","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/TASE.2008.2005640","volume":"7","author":"H-X Tian","year":"2010","unstructured":"Tian H-X, Mao Z-Z (2010) An ensemble ELM based on modified AdaBoost. RT algorithm for predicting the temperature of molten steel in ladle furnace. IEEE Trans Autom Sci Eng 7(1):73\u201380","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"Nov","key":"7543_CR95","first-page":"45","volume":"2","author":"S Tong","year":"2001","unstructured":"Tong S, Koller D (2001) Support vector machine active learning with applications to text classification. J Mach Learn Res 2(Nov):45\u201366","journal-title":"J Mach Learn Res"},{"key":"7543_CR96","first-page":"3103","volume":"13","author":"Z Wang","year":"2012","unstructured":"Wang Z, Crammer K, Vucetic S (2012) Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale svm training. J Mach Learn Res 13:3103\u20133131","journal-title":"J Mach Learn Res"},{"issue":"2","key":"7543_CR97","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s00521-015-1874-3","volume":"27","author":"G-G Wang","year":"2015","unstructured":"Wang G-G, Lu M, Dong Y-Q et al (2015a) Self-adaptive extreme learning machine. Neural Comput & Applic 27(2):291\u2013303. \nhttps:\/\/doi.org\/10.1007\/s00521-015-1874-3","journal-title":"Neural Comput & Applic"},{"key":"7543_CR98","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.neucom.2014.04.073","volume":"149","author":"Y Wang","year":"2015","unstructured":"Wang Y, Li D, Du Y et al (2015b) Anomaly detection in traffic using L1-norm minimization extreme learning machine. Neurocomputing 149:415\u2013425. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2014.04.073","journal-title":"Neurocomputing"},{"key":"7543_CR99","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.neucom.2015.03.114","volume":"174","author":"L Wang","year":"2016","unstructured":"Wang L, Liu H, Sun F (2016) Dynamic texture video classification using extreme learning machine. Neurocomputing 174:278\u2013285. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2015.03.114","journal-title":"Neurocomputing"},{"key":"7543_CR100","unstructured":"Wenhua X, Zheng Q, Yang C (2011) A framework for classifying uncertain and evolving data stream. Inf Technol J:1926\u20131933"},{"key":"7543_CR101","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neucom.2015.11.095","volume":"190","author":"D Wu","year":"2016","unstructured":"Wu D, Wang Z, Chen Y et al (2016) Mixed-kernel based weighted extreme learning machine for inertial sensor based human activity recognition with imbalanced dataset. Neurocomputing 190:35\u201349. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2015.11.095","journal-title":"Neurocomputing"},{"key":"7543_CR102","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2016.09.120","volume":"261","author":"W Xiao","year":"2017","unstructured":"Xiao W, Zhang J, Li Y et al (2017) Class-specific cost regulation extreme learning machine for imbalanced classification. Neurocomputing 261:70\u201382. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2016.09.120","journal-title":"Neurocomputing"},{"key":"7543_CR103","unstructured":"Xin Y, Fei L, Jingshuai W (2017) Signal Detection of MIMO-OFDM System Based on Auto Encoder and Extreme Learning Machine. In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE"},{"issue":"2","key":"7543_CR104","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1109\/26.983313","volume":"50","author":"Z Xu","year":"2002","unstructured":"Xu Z, Murch RD (2002) Performance analysis of maximum likelihood detection in a MIMO antenna system. IEEE Trans Commun 50(2):187\u2013191. \nhttps:\/\/doi.org\/10.1109\/26.983313","journal-title":"IEEE Trans Commun"},{"key":"7543_CR105","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.eswa.2016.08.052","volume":"65","author":"S Xu","year":"2016","unstructured":"Xu S, Wang J (2016) A fast incremental extreme learning machine algorithm for data streams classification. Expert Syst Appl 65:332\u2013344","journal-title":"Expert Syst Appl"},{"key":"7543_CR106","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.neucom.2016.12.078","volume":"238","author":"S Xu","year":"2017","unstructured":"Xu S, Wang J (2017) Dynamic extreme learning machine for data stream classification. Neurocomputing 238:433\u2013449","journal-title":"Neurocomputing"},{"key":"7543_CR107","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.neucom.2013.09.042","volume":"129","author":"X Xue","year":"2014","unstructured":"Xue X, Yao M, Wu Z et al (2014) Genetic ensemble of extreme learning machine. Neurocomputing 129:175\u2013184","journal-title":"Neurocomputing"},{"key":"7543_CR108","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.measurement.2016.06.042","volume":"92","author":"B Yadav","year":"2016","unstructured":"Yadav B, Ch S, Mathur S et al (2016) Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: a case study in Neckar River, Germany. Measurement 92:433\u2013445. \nhttps:\/\/doi.org\/10.1016\/j.measurement.2016.06.042","journal-title":"Measurement"},{"issue":"7","key":"7543_CR109","doi-asserted-by":"publisher","first-page":"107","DOI":"10.3390\/a11070107","volume":"11","author":"R Yang","year":"2018","unstructured":"Yang R, Xu S, Feng L (2018) An ensemble extreme learning machine for data stream classification. Algorithms 11(7):107. \nhttps:\/\/doi.org\/10.3390\/a11070107","journal-title":"Algorithms"},{"issue":"3","key":"7543_CR110","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1007\/s12555-013-0238-y","volume":"12","author":"CH Yu","year":"2014","unstructured":"Yu CH, Choi JW (2014) Interacting multiple model filter-based distributed target tracking algorithm in underwater wireless sensor networks. Int J Control Autom Syst 12(3):618\u2013627. \nhttps:\/\/doi.org\/10.1007\/s12555-013-0238-y","journal-title":"Int J Control Autom Syst"},{"key":"7543_CR111","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neucom.2015.04.019","volume":"166","author":"H Yu","year":"2015","unstructured":"Yu H, Sun C, Yang W et al (2015) AL-ELM: one uncertainty-based active learning algorithm using extreme learning machine. Neurocomputing 166:140\u2013150. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2015.04.019","journal-title":"Neurocomputing"},{"issue":"4","key":"7543_CR112","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TNNLS.2018.2855446","volume":"30","author":"Hualong Yu","year":"2019","unstructured":"Yu H, Yang X, Zheng S et al (2018) Active learning from imbalanced data: a solution of online weighted extreme learning machine. IEEE Trans Neural Netw Learn Syst. \nhttps:\/\/doi.org\/10.1109\/TNNLS.2018.2855446","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"9","key":"7543_CR113","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1007\/s00500-012-0824-6","volume":"16","author":"J-h Zhai","year":"2012","unstructured":"Zhai J-h, H-y X, X-z W (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16(9):1493\u20131502. \nhttps:\/\/doi.org\/10.1007\/s00500-012-0824-6","journal-title":"Soft Comput"},{"key":"7543_CR114","doi-asserted-by":"crossref","unstructured":"Zhang P, Zhu X, Shi Y et al (2009) An aggregate ensemble for mining concept drifting data streams with noise. In Pacific-Asia Conference on Knowledge Discovery and Data Mining:1021-1029. Springer","DOI":"10.1007\/978-3-642-01307-2_109"},{"issue":"2","key":"7543_CR115","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.3233\/JIFS-16724","volume":"33","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Liu W, Ren X et al (2017) Dual weighted extreme learning machine for imbalanced data stream classification. Journal of Intelligent & Fuzzy Systems 33(2):1143\u20131154","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"7543_CR116","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.neucom.2012.02.003","volume":"87","author":"J Zhao","year":"2012","unstructured":"Zhao J, Wang Z, Park DS (2012) Online sequential extreme learning machine with forgetting mechanism. Neurocomputing 87:79\u201389. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2012.02.003","journal-title":"Neurocomputing"},{"key":"7543_CR117","doi-asserted-by":"crossref","unstructured":"Zhenyu C, Shuangquan W, Zhiqi S (2013) Online Sequential ELM based Transfer Learning for Transportation Mode Recognition. In 2013 IEEE Conference on Cybernetics and Intelligent Systems (CIS). IEEE","DOI":"10.1109\/ICCIS.2013.6751582"},{"key":"7543_CR118","unstructured":"Zhiyuan M, Guangchun L, Huang D (2016) Short Term Traffic Flow Prediction Based on Online Sequential Extreme Learning Machine. In 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI). IEEE"},{"issue":"5","key":"7543_CR119","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1007\/s12555-012-9511-8","volume":"11","author":"W Zhou","year":"2013","unstructured":"Zhou W, Ji C, Mou J et al (2013) Adaptive target synchronization for wireless sensor networks with Markov delays and noise perturbation. Int J Control Autom Syst 11(5):919\u2013925. \nhttps:\/\/doi.org\/10.1007\/s12555-012-9511-8","journal-title":"Int J Control Autom Syst"},{"key":"7543_CR120","unstructured":"Zhu J, Hovy E (2007) Active learning for word sense disambiguation with methods for addressing the class imbalance problem. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)"},{"key":"7543_CR121","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.neucom.2012.08.010","volume":"101","author":"W Zong","year":"2013","unstructured":"Zong W, Huang G-B, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229\u2013242. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2012.08.010","journal-title":"Neurocomputing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-7543-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-019-7543-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-7543-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T23:27:19Z","timestamp":1587684439000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-019-7543-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,25]]},"references-count":121,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["7543"],"URL":"https:\/\/doi.org\/10.1007\/s11042-019-7543-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,25]]},"assertion":[{"value":"25 September 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}