{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T06:07:31Z","timestamp":1747375651261,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100010801","name":"xunta de galicia","doi-asserted-by":"publisher","award":["2019-2022 ED431G-2019\/04"],"award-info":[{"award-number":["2019-2022 ED431G-2019\/04"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s00521-021-06727-8","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:03:57Z","timestamp":1642464237000},"page":"5923-5938","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Quick extreme learning machine for large-scale classification"],"prefix":"10.1007","volume":"34","author":[{"given":"Audi","family":"Albtoush","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5483-9424","authenticated-orcid":false,"given":"Manuel","family":"Fern\u00e1ndez-Delgado","sequence":"additional","affiliation":[]},{"given":"Eva","family":"Cernadas","sequence":"additional","affiliation":[]},{"given":"Sen\u00e9n","family":"Barro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"issue":"2","key":"6727_CR1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10044-020-00891-8","volume":"24","author":"A Afzal","year":"2021","unstructured":"Afzal A, Nair N, Asharaf S (2021) Deep kernel learning in extreme learning machines. Patt Anal Appl 24(2):11\u201319","journal-title":"Patt Anal Appl"},{"key":"6727_CR2","doi-asserted-by":"crossref","first-page":"33789","DOI":"10.1109\/ACCESS.2018.2841987","volume":"6","author":"I Ahmad","year":"2018","unstructured":"Ahmad I, Basheri M, Iqbal M, Rahim A (2018) Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6:33789\u201333795","journal-title":"IEEE Access"},{"key":"6727_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3085969","author":"Z Akram-Ali-Hammouri","year":"2021","unstructured":"Akram-Ali-Hammouri Z, Fern\u00e1ndez-Delgado M, Cernadas E, Barro S (2021) Fast support vector classification for large-scale problems. IEEE T Pat Anal. https:\/\/doi.org\/10.1109\/TPAMI.2021.3085969","journal-title":"IEEE T Pat Anal"},{"key":"6727_CR4","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/ACCESS.2015.2450498","volume":"3","author":"A Akusok","year":"2015","unstructured":"Akusok A, Bj\u00f6rk K, Miche Y, Lendasse A (2015) High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access 3:1011\u20131025","journal-title":"IEEE Access"},{"key":"6727_CR5","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2012.02.042","volume":"102","author":"F Cao","year":"2013","unstructured":"Cao F, Liu B, Park D (2013) Image classification based on effective extreme learning machine. Neurocomputing 102:90\u201397","journal-title":"Neurocomputing"},{"key":"6727_CR6","first-page":"1","volume":"2015","author":"J Cao","year":"2013","unstructured":"Cao J, Lin Z (2013) Extreme learning machines on high dimensional and large data applications: a survey. Math Probl Eng 2015:1\u201313","journal-title":"Math Probl Eng"},{"issue":"2","key":"6727_CR7","first-page":"249","volume":"22","author":"J Carletta","year":"1996","unstructured":"Carletta J (1996) Assessing agreement on classification tasks: the kappa statistic. Comput Linguist 22(2):249\u2013254","journal-title":"Comput Linguist"},{"key":"6727_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C Chang","year":"2011","unstructured":"Chang C, Lin C (2011) LIBSVM: a library for support vector machines. ACM T Intel Syst Tech 2:1\u201327","journal-title":"ACM T Intel Syst Tech"},{"key":"6727_CR9","volume-title":"Statistics in medicine","author":"T Colton","year":"1974","unstructured":"Colton T (1974) Statistics in medicine. Little Brown and Co"},{"key":"6727_CR10","doi-asserted-by":"crossref","unstructured":"Coppersmith D, Winograd S (1987) Matrix multiplication via arithmetic progressions. In: Proc ACM Sympos Theory Comput. pp. 1\u20136","DOI":"10.1145\/28395.28396"},{"key":"6727_CR11","first-page":"25","volume":"8","author":"P Courrieu","year":"2005","unstructured":"Courrieu P (2005) Fast computation of Moore-Penrose inverse matrices. Neu Inf Pro Lett Rev 8:25\u201329","journal-title":"Neu Inf Pro Lett Rev"},{"key":"6727_CR12","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neunet.2015.09.003","volume":"77","author":"W Deng","year":"2016","unstructured":"Deng W, Bai Z, Huang G, Zheng Q (2016) A fast SVD-hidden-nodes based extreme learning machine for large-scale data analytics. Neural Netw 77:14\u201328","journal-title":"Neural Netw"},{"issue":"1","key":"6727_CR13","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11063-017-9700-9","volume":"48","author":"M Dias","year":"2018","unstructured":"Dias M, de Sousa L, Neto A, Freire A (2018) Fixed-size extreme learning machines through simulated annealing. Neural Process Lett 48(1):135\u2013151","journal-title":"Neural Process Lett"},{"issue":"6","key":"6727_CR14","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TNNLS.2017.2654357","volume":"29","author":"M Duan","year":"2018","unstructured":"Duan M, Li K, Liao X, Li K (2018) A parallel multiclassification algorithm for big data using an extreme learning machine. IEEE T Neur Net Lear 29(6):2337\u20132351","journal-title":"IEEE T Neur Net Lear"},{"issue":"4","key":"6727_CR15","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.3906\/elk-1606-122","volume":"25","author":"\u00d6F Ertu\u011frul","year":"2017","unstructured":"Ertu\u011frul \u00d6F, Ta\u011fluk ME (2017) A fast feature selection approach based on extreme learning machine and coefficient of variation. Turk J Elec Eng Comput Sci 25(4):3409\u20133420","journal-title":"Turk J Elec Eng Comput Sci"},{"key":"6727_CR16","first-page":"1871","volume":"9","author":"R Fan","year":"2008","unstructured":"Fan R, Chang K, Hsieh C, Wang X, Lin C (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871\u20131874","journal-title":"J Mach Learn Res"},{"issue":"4","key":"6727_CR17","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1007\/s00521-016-2503-5","volume":"29","author":"S Hassan","year":"2018","unstructured":"Hassan S, Khanesar M, Jaafar J, Khosravi A (2018) Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm. Neural Comput Appl 29(4):1001\u20131014","journal-title":"Neural Comput Appl"},{"key":"6727_CR18","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neucom.2012.01.040","volume":"102","author":"Q He","year":"2013","unstructured":"He Q, Shang T, Zhuang F, Shi Z (2013) Parallel extreme learning machine for regression based on MapReduce. Neurocomputing 102:52\u201358","journal-title":"Neurocomputing"},{"key":"6727_CR19","doi-asserted-by":"crossref","unstructured":"Hsu H, Chang C, Hsu C (2017) Chapter 6: extreme learning machine and its applications in big data processing. In: Big data analytics for sensor-network collected intelligence, Intelligent Data-Centric Systems, pp. 117\u2013150. Academic Press","DOI":"10.1016\/B978-0-12-809393-1.00006-4"},{"key":"6727_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2015.06.002","volume":"70","author":"G Huang","year":"2015","unstructured":"Huang G, Liu T, Yang Y, Lin Z, Song S, Wu C (2015) Discriminative clustering via extreme learning machine. Neural Netw 70:1\u20138","journal-title":"Neural Netw"},{"issue":"2","key":"6727_CR21","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2011","unstructured":"Huang GB, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE T Syst Man Cy B 42(2):513\u2013529","journal-title":"IEEE T Syst Man Cy B"},{"key":"6727_CR22","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.procs.2015.07.319","volume":"53","author":"A Iosifidis","year":"2015","unstructured":"Iosifidis A, Tefas A, Pitas I (2015) Regularized extreme learning machine for large-scale media content analysis. Procedia Comput Sci 53:420\u2013427","journal-title":"Procedia Comput Sci"},{"key":"6727_CR23","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.neucom.2016.09.023","volume":"219","author":"A Iosifidis","year":"2017","unstructured":"Iosifidis A, Tefas A, Pitas I (2017) Approximate kernel extreme learning machine for large scale data classification. Neurocomputing 219:210\u2013220","journal-title":"Neurocomputing"},{"key":"6727_CR24","doi-asserted-by":"crossref","unstructured":"Khan K, Ratner E, Ludwig R, Lendasse A (2020) Feature bagging and extreme learning machines: machine learning with severe memory constraints. In: Proc Intl J Conf Neur Netw, pp. 1\u20137","DOI":"10.1109\/IJCNN48605.2020.9207673"},{"key":"6727_CR25","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1007\/s11063-021-10465-2","volume":"53","author":"B K\u00f6kt\u00fcrk-G\u00fczel","year":"2021","unstructured":"K\u00f6kt\u00fcrk-G\u00fczel B, Beyhan S (2021) Symbolic regression based extreme learning machine models for system identification. Neural Proc Lett 53:1565\u20131578","journal-title":"Neural Proc Lett"},{"key":"6727_CR26","doi-asserted-by":"crossref","first-page":"46055","DOI":"10.1109\/ACCESS.2020.2978648","volume":"8","author":"Y Kongsorot","year":"2020","unstructured":"Kongsorot Y, Horata P, Musikawan P (2020) An incremental kernel extreme learning machine for multi-label learning with emerging new labels. IEEE Access 8:46055\u201346070","journal-title":"IEEE Access"},{"issue":"13\u201315","key":"6727_CR27","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.1016\/j.neucom.2009.02.013","volume":"72","author":"Y Lan","year":"2009","unstructured":"Lan Y, Soh Y, Huang G (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72(13\u201315):3391\u20133395","journal-title":"Neurocomputing"},{"issue":"13\u201315","key":"6727_CR28","first-page":"3391","volume":"72","author":"M Larrea","year":"2009","unstructured":"Larrea M, Porto A, Irigoyen E, Barrag\u00e1n A, And\u00fajar J (2009) Extreme learning machine ensemble model for time series forecasting boosted by PSO: application to an electric consumption problem. Neurocomputing 72(13\u201315):3391\u20133395","journal-title":"Neurocomputing"},{"key":"6727_CR29","doi-asserted-by":"crossref","first-page":"14385","DOI":"10.1007\/s00521-019-04481-6","volume":"32","author":"L Li","year":"2020","unstructured":"Li L, Wang G, Wu G, Zhang Q (2020) An experimental evaluation of extreme learning machines on several hardware devices. Neural Comput Appl 32:14385\u201314397","journal-title":"Neural Comput Appl"},{"issue":"6","key":"6727_CR30","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","volume":"17","author":"N Liang","year":"2006","unstructured":"Liang N, Huang G, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE T Neural Networ 17(6):1411\u20131423","journal-title":"IEEE T Neural Networ"},{"key":"6727_CR31","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neunet.2012.04.002","volume":"33","author":"X Liu","year":"2012","unstructured":"Liu X, Gao C, Li P (2012) A comparative analysis of support vector machines and extreme learning machines. Neural Netw 33:58\u201366","journal-title":"Neural Netw"},{"key":"6727_CR32","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.engappai.2016.06.015","volume":"55","author":"C Lu","year":"2016","unstructured":"Lu C, Kao L (2016) A clustering-based sales forecasting scheme by using extreme learning machine and ensembling linkage methods with applications to computer server. Eng Appl Artif Intel 55:231\u2013238","journal-title":"Eng Appl Artif Intel"},{"key":"6727_CR33","doi-asserted-by":"crossref","unstructured":"Lu S, Zhang G, Wang X (2012) A rank reduced matrix method in extreme learning machine. In: J.\u00a0Wang, G.\u00a0Yen, M.\u00a0Polycarpou (eds.) Advances in Neural Networks-ISNN 2012, pp. 72\u201379","DOI":"10.1007\/978-3-642-31346-2_9"},{"key":"6727_CR34","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.neucom.2020.07.151","volume":"438","author":"J L\u00f3pez-Guede","year":"2021","unstructured":"L\u00f3pez-Guede J, Izquierdo A, Est\u00e9vez J, Gra\u00f1a M (2021) Active learning for road lane landmark inventory with V-ELM in highly uncontrolled image capture conditions. Neurocomputing 438:259\u2013269","journal-title":"Neurocomputing"},{"issue":"5","key":"6727_CR35","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1109\/LCOMM.2021.3049885","volume":"25","author":"Z Mai","year":"2021","unstructured":"Mai Z, Chen Y, Du L (2021) A novel blind mmwave channel estimation algorithm based on ML-ELM. IEEE Commun Lett 25(5):1549\u20131553","journal-title":"IEEE Commun Lett"},{"key":"6727_CR36","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1007\/s00521-013-1392-0","volume":"24","author":"W Mao","year":"2014","unstructured":"Mao W, Zheng Y, Mu X, Zhao J (2014) Uncertainty evaluation and model selection of extreme learning machine based on Riemannian metric. Neur Comput Appl 24:1613\u20131625","journal-title":"Neur Comput Appl"},{"key":"6727_CR37","doi-asserted-by":"crossref","unstructured":"Merkel C, Kudithipudi D (2014) Neuromemristive extreme learning machines for pattern classification. In: 2014 IEEE Comput Soc Ann Sympos on VLSI, pp. 77\u201382","DOI":"10.1109\/ISVLSI.2014.67"},{"key":"6727_CR38","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.neucom.2021.03.110","volume":"448","author":"T Ouyang","year":"2021","unstructured":"Ouyang T (2021) Feature learning for stacked ELM via low-rank matrix factorization. Neurocomputing 448:82\u201393","journal-title":"Neurocomputing"},{"issue":"17","key":"6727_CR39","doi-asserted-by":"crossref","first-page":"13805","DOI":"10.1007\/s00521-020-04788-9","volume":"32","author":"C Perales-Gonz\u00e1lez","year":"2020","unstructured":"Perales-Gonz\u00e1lez C, Carbonero-Ruz M, P\u00e9rez-Rodr\u00edguez J, Becerra-Alonso D, Fern\u00e1ndez-Navarro F (2020) Negative correlation learning in the extreme learning machine framework. Neural Comput Appl 32(17):13805\u201313823","journal-title":"Neural Comput Appl"},{"key":"6727_CR40","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1007\/BF02165411","volume":"13","author":"V Strassen","year":"1969","unstructured":"Strassen V (1969) Gaussian elimination is not optimal. Numer Math 13:354\u2013356","journal-title":"Numer Math"},{"issue":"1","key":"6727_CR41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00406-6","volume":"8","author":"N Surantha","year":"2021","unstructured":"Surantha N, Lesmana T, Isa S (2021) Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data. J Big Data 8(1):1\u201317","journal-title":"J Big Data"},{"key":"6727_CR42","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.neucom.2017.04.036","volume":"260","author":"Y Wan","year":"2017","unstructured":"Wan Y, Song S, Huang G, Li S (2017) Twin extreme learning machines for pattern classification. Neurocomputing 260:235\u2013244","journal-title":"Neurocomputing"},{"key":"6727_CR43","doi-asserted-by":"crossref","unstructured":"Wang H, He Q, Shang T, Zhuang F, Shi Z (2015) Extreme learning machine ensemble classifier for large-scale data. In: Proc ELM-2014, vol.\u00a01, pp. 151\u2013161. Springer","DOI":"10.1007\/978-3-319-14063-6_14"},{"key":"6727_CR44","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1016\/j.neucom.2010.11.030","volume":"74","author":"Y Wang","year":"2011","unstructured":"Wang Y, Cao F, Yuan Y (2011) A study on effectiveness of extreme learning machine. Neurocomputing 74:2483\u20132490","journal-title":"Neurocomputing"},{"key":"6727_CR45","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.neucom.2013.09.075","volume":"149","author":"J Xin","year":"2019","unstructured":"Xin J, Wang Z, Qu L, Wang G (2019) Elastic extreme learning machine for big data classification. Neurocomputing 149:464\u2013471","journal-title":"Neurocomputing"},{"issue":"6(730)","key":"6727_CR46","first-page":"1","volume":"11","author":"Y Xing","year":"2019","unstructured":"Xing Y, Ban X, Liu X, Shen Q (2019) Large-scale traffic congestion prediction based on the symmetric extreme learning machine cluster fast learning method. Symmetry 11(6(730)):1\u201319","journal-title":"Symmetry"},{"issue":"2","key":"6727_CR47","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s11063-015-9470-1","volume":"44","author":"J Zhai","year":"2016","unstructured":"Zhai J, Shao Q, Wang X (2016) Architecture selection of ELM networks based on sensitivity of hidden nodes. Neural Process Lett 44(2):471\u2013489","journal-title":"Neural Process Lett"},{"key":"6727_CR48","doi-asserted-by":"crossref","unstructured":"Zhai J, Wang J, Wang X (2014) Ensemble online sequential extreme learning machine for large data set classification. In: Proc IEEE Intl Conf Syst Man Cyb, pp. 2250\u20132255","DOI":"10.1109\/SMC.2014.6974260"},{"key":"6727_CR49","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1007\/s13042-015-0478-7","volume":"8","author":"J Zhai","year":"2017","unstructured":"Zhai J, Zhang S, Wang C (2017) The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers. Intl J Mach Learn Cyber 8:1009\u20131017","journal-title":"Intl J Mach Learn Cyber"},{"key":"6727_CR50","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1007\/s11063-021-10435-8","volume":"53","author":"S Zhu","year":"2021","unstructured":"Zhu S, Wang H, Lv H, Zhang H (2021) Augmented online sequential quaternion extreme learning machine. Neural Process Lett 53:1161\u20131186","journal-title":"Neural Process Lett"},{"key":"6727_CR51","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.neucom.2012.08.010","volume":"101","author":"W Zong","year":"2013","unstructured":"Zong W, Huang G, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229\u2013242","journal-title":"Neurocomputing"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06727-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06727-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06727-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T14:17:58Z","timestamp":1647440278000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06727-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,18]]},"references-count":51,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6727"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06727-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,1,18]]},"assertion":[{"value":"14 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}