{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:29:00Z","timestamp":1765232940726},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"37-38","license":[{"start":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T00:00:00Z","timestamp":1595635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T00:00:00Z","timestamp":1595635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s11042-019-08321-6","type":"journal-article","created":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T20:03:45Z","timestamp":1595707425000},"page":"27439-27464","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fuzzy ELM for classification based on feature space"],"prefix":"10.1007","volume":"79","author":[{"given":"Yonghe","family":"Chu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaowu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Diao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deqin","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,25]]},"reference":[{"key":"8321_CR1","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository. In: School Inf. Comput. Sci., Univ. California, Irvine, CA, USA. Available: http:\/\/archive.ics.uci.edu\/ml"},{"issue":"2","key":"8321_CR2","first-page":"60","volume":"50","author":"S Barro","year":"2014","unstructured":"Barro S, Ribeiro J (2014) Direct kernel perceptron (DKP): ultra-fast kernel ELM-based classification with noniterative closed-form weight calculation. Neural Netw 50(2):60\u201371","journal-title":"Neural Netw"},{"key":"8321_CR3","doi-asserted-by":"publisher","unstructured":"Cao FX, Yang ZJ, Ren JC, Jiang MY, Ling WK (2017) Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images. Sensors. https:\/\/doi.org\/10.3390\/s17112603","DOI":"10.3390\/s17112603"},{"issue":"3","key":"8321_CR4","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/s11063-012-9253-x","volume":"37","author":"A Casta\u00f1o","year":"2013","unstructured":"Casta\u00f1o A, Fern\u00e1ndez-Navarro F, Herv\u00e1s-Mart\u00ednez C (2013) PCA-ELM: a robust and pruned extreme learning machine approach based on principal component analysis. Neural Process Lett 37(3):377\u2013392","journal-title":"Neural Process Lett"},{"issue":"C","key":"8321_CR5","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.neucom.2015.04.011","volume":"166","author":"Y Gu","year":"2015","unstructured":"Gu Y, Chen YQ, Liu JF, Jiang XL (2015) Semi-supervised deep extreme learning machine for Wi-Fi based localization. Neurocomputing 166(C):282\u2013293","journal-title":"Neurocomputing"},{"issue":"3","key":"8321_CR6","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","volume":"6","author":"GB Huang","year":"2014","unstructured":"Huang GB (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6(3):376\u2013390","journal-title":"Cogn Comput"},{"issue":"12","key":"8321_CR7","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1109\/TCYB.2014.2307349","volume":"44","author":"G Huang","year":"2014","unstructured":"Huang G, Song SJ (2014) Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern 44(12):2405\u20132417","journal-title":"IEEE Trans Cybern"},{"key":"8321_CR8","unstructured":"Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks, IJCNN2004, Budapest, Hungary, 2, pp 985\u2013990"},{"issue":"1","key":"8321_CR9","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"issue":"4","key":"8321_CR10","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TNN.2006.875977","volume":"17","author":"GB Huang","year":"2006","unstructured":"Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879\u2013892","journal-title":"IEEE Trans Neural Netw"},{"issue":"3","key":"8321_CR11","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1109\/TCSII.2005.857540","volume":"53","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Mao KZ, Siew CK, Saratchandran P, Sundararajan N (2006) Can threshold networks be trained directly? IEEE Trans Circuits Syst Express Briefs 53(3):187\u2013191","journal-title":"IEEE Trans Circuits Syst Express Briefs"},{"issue":"2","key":"8321_CR12","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, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513\u2013529","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"issue":"3\u20134","key":"8321_CR13","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1007\/s00521-006-0028-z","volume":"15","author":"XF Jiang","year":"2006","unstructured":"Jiang XF, Yi Z, Lv JC (2006) Fuzzy SVM with a new fuzzy membership function. Neural Comput & Applic 15(3\u20134):268\u2013276","journal-title":"Neural Comput & Applic"},{"key":"8321_CR14","doi-asserted-by":"crossref","unstructured":"Lavneet S, Girija C, Dharmendra S (2012) A novel approach to protein structure prediction using PCA based extreme learning machines and multiple kernels. In: International conference on algorithms and architectures for parallel processing, pp 292\u2013299","DOI":"10.1007\/978-3-642-33065-0_31"},{"key":"8321_CR15","doi-asserted-by":"publisher","first-page":"1725","DOI":"10.1016\/j.neucom.2017.09.004","volume":"275","author":"L Li","year":"2018","unstructured":"Li L, Wang CY, Li W, Chen JB (2018) Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines. Neurocomputing 275:1725\u20131733","journal-title":"Neurocomputing"},{"issue":"1","key":"8321_CR16","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TNNLS.2014.2336665","volume":"26","author":"S Lin","year":"2015","unstructured":"Lin S, Liu X, Fang J, Xu Z (2015) Is extreme learning machine feasible? A theoretical assessment (part II). IEEE Trans Neural Netw Learn Syst 26(1):21\u201334","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"8321_CR17","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.neunet.2012.04.002","volume":"33","author":"XY Liu","year":"2012","unstructured":"Liu XY, Gao CH, Li P (2012) A comparative analysis of support vector machines and extreme learning machines. Neural Netw 33(9):58\u201366","journal-title":"Neural Netw"},{"issue":"1","key":"8321_CR18","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.neucom.2014.04.041","volume":"144","author":"S Liu","year":"2014","unstructured":"Liu S, Feng L, Xiao Y (2014) Robust activation function and its application: semi-supervised kernel extreme learning method. Neurocomputing 144(1):318\u2013328","journal-title":"Neurocomputing"},{"issue":"1","key":"8321_CR19","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/TNNLS.2014.2335212","volume":"26","author":"X Liu","year":"2015","unstructured":"Liu X, Lin S, Fang J, Xu Z (2015) Is extreme learning machine feasible? a theoretical assessment (part I). IEEE Trans Neural Netw Learn Syst 26(1):7\u201320","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"8321_CR20","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00521-014-1777-8","volume":"27","author":"B Liu","year":"2016","unstructured":"Liu B, Xia SX, Meng FR, Zhou Y (2016) Manifold regularized extreme learning machine. Neural Comput & Applic 27(2):255\u2013269","journal-title":"Neural Comput & Applic"},{"issue":"99","key":"8321_CR21","first-page":"1725","volume":"5","author":"F Lv","year":"2017","unstructured":"Lv F, Han M, Qiu T (2017) Remote sensing image classification based on ensemble extreme learning machine with stacked autoencoder. IEEE Access 5(99):1725\u20131733","journal-title":"IEEE Access"},{"key":"8321_CR22","first-page":"214","volume":"334","author":"YP Ma","year":"2018","unstructured":"Ma YP, Niu PF, Yan SS, Li GQ (2018) A modified online sequential extreme learning machine for building circulation fluidized bed boiler's NOx emission model. Appl Math Comput 334:214\u2013226","journal-title":"Appl Math Comput"},{"key":"8321_CR23","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.neunet.2018.05.011","volume":"105","author":"BS Raghuwanshi","year":"2018","unstructured":"Raghuwanshi BS, Shukla S (2018) Class-specific extreme learning machine for handling binary class imbalance problem. Neural Netw 105:206\u2013217","journal-title":"Neural Netw"},{"key":"8321_CR24","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.engappai.2018.07.002","volume":"74","author":"BS Raghuwanshi","year":"2018","unstructured":"Raghuwanshi BS, Shukla S (2018) UnderBagging based reduced Kernelized weighted extreme learning machine for class imbalance learning. Eng Appl Artif Intell 74:252\u2013270","journal-title":"Eng Appl Artif Intell"},{"issue":"6088","key":"8321_CR25","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"D Rumelhart","year":"1986","unstructured":"Rumelhart D, Hinton G, Williams R (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536","journal-title":"Nature"},{"key":"8321_CR26","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.neucom.2018.03.056","volume":"310","author":"M Sahani","year":"2018","unstructured":"Sahani M, Dash PK (2018) Variational mode decomposition and weighted online sequential extreme learning machine for power quality event patterns accuracy. Neurocomputing 310:10\u201327","journal-title":"Neurocomputing"},{"issue":"1","key":"8321_CR27","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1109\/JSTARS.2016.2591004","volume":"10","author":"H Su","year":"2017","unstructured":"Su H, Cai Y, Du Q (2017) Firefly-algorithm-inspired framework with band selection and extreme learning machine for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 10(1):309\u2013320","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"3","key":"8321_CR28","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s10115-009-0220-4","volume":"23","author":"XL Tang","year":"2010","unstructured":"Tang XL, Han M (2010) Ternary reversible extreme learning machines: The incremental tri-training method for semi-supervised classification. Knowl Inf Syst 23(3):345\u2013372","journal-title":"Knowl Inf Syst"},{"issue":"6","key":"8321_CR29","first-page":"988","volume":"8","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN (1995) The nature of statistical learning theory. Springer 8(6):988\u2013999","journal-title":"Springer"},{"issue":"2","key":"8321_CR30","first-page":"3","volume":"102","author":"XZ Wang","year":"2013","unstructured":"Wang XZ, Shao QY, Miao Q, Zhai JH (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102(2):3\u20139","journal-title":"Neurocomputing"},{"issue":"PA","key":"8321_CR31","first-page":"18","volume":"174","author":"Q Wang","year":"2016","unstructured":"Wang Q, Wang WG, Nian R, He B (2016) Manifold learning in local tangent space via extreme learning machine. Neurocomputing 174(PA):18\u201330","journal-title":"Neurocomputing"},{"issue":"1","key":"8321_CR32","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s12559-014-9256-1","volume":"7","author":"SX Xia","year":"2015","unstructured":"Xia SX, Meng FR, Liu B, Zhou Y (2015) A kernel clustering-based possibilistic fuzzy extreme learning machine for class imbalance learning. Cogn Comput 7(1):74\u201385","journal-title":"Cogn Comput"},{"key":"8321_CR33","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.neucom.2016.09.120","volume":"261","author":"WD Xiao","year":"2017","unstructured":"Xiao WD, Zhang J, Li YJ, Zhang S, Yang WD (2017) Class-specific cost regulation extreme learning machine for imbalanced classification. Neurocomputing 261:70\u201382","journal-title":"Neurocomputing"},{"issue":"2","key":"8321_CR34","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.neucom.2012.02.040","volume":"102","author":"Q Yu","year":"2013","unstructured":"Yu Q, Miche Y, Eirola E, van Heeswijk M, Severin E, Lendasse A (2013) Regularized extreme learning machine for regression with missing data. Neurocomputing 102(2):45\u201351","journal-title":"Neurocomputing"},{"issue":"7","key":"8321_CR35","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1049\/el.2012.3642","volume":"49","author":"W Zhang","year":"2013","unstructured":"Zhang W, Ji H (2013) Fuzzy extreme learning machine for classification. Electron Lett 49(7):448\u2013450","journal-title":"Electron Lett"},{"key":"8321_CR36","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1016\/j.neucom.2014.09.022","volume":"151","author":"K Zhang","year":"2015","unstructured":"Zhang K, Luo MX (2015) Outlier-robust extreme learning machine for regression problems. Neurocomputing 151:1519\u20131527","journal-title":"Neurocomputing"},{"issue":"2","key":"8321_CR37","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1109\/TNNLS.2011.2178124","volume":"23","author":"R Zhang","year":"2012","unstructured":"Zhang R, Lan Y, Huang GB, Xu ZB (2012) Universal approximation of extreme learning machine with adaptive growth of hidden nodes. IEEE Trans Neural Netw Learn Syst 23(2):365\u2013371","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"8321_CR38","doi-asserted-by":"crossref","unstructured":"Zheng EH, Liu JY (2013) A new fuzzy extreme learning machine for regression problems with outliers or noises, 9th international conference, ADMA, pp 524\u2013534","DOI":"10.1007\/978-3-642-53917-6_47"},{"issue":"3-4","key":"8321_CR39","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s00521-011-0808-y","volume":"22","author":"WB Zheng","year":"2013","unstructured":"Zheng WB, Qian YT, Lu HJ (2013) Text categorization based on regularization extreme learning machine. Neural Comput & Applic 22(3-4):447\u2013456","journal-title":"Neural Comput & Applic"},{"key":"8321_CR40","doi-asserted-by":"crossref","unstructured":"Zhu WT, Miao J, Qing LY (2014) Constrained extreme learning machine: a novel highly discriminative random feedforward neural network. In: International joint conference on neural networks, pp 6\u201311","DOI":"10.1109\/IJCNN.2014.6889761"},{"key":"8321_CR41","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1016\/j.neucom.2017.11.030","volume":"275","author":"QY Zou","year":"2018","unstructured":"Zou QY, Wang XJ, Zhou CJ, Zhang Q (2018) The memory degradation based online sequential extreme learning machine. Neurocomputing 275:2864\u20132879","journal-title":"Neurocomputing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-08321-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-019-08321-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-08321-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,24]],"date-time":"2021-07-24T23:28:24Z","timestamp":1627169304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-019-08321-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,25]]},"references-count":41,"journal-issue":{"issue":"37-38","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["8321"],"URL":"https:\/\/doi.org\/10.1007\/s11042-019-08321-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,25]]},"assertion":[{"value":"5 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}