{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T11:54:46Z","timestamp":1723031686877},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11063-022-11021-2","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T09:02:56Z","timestamp":1662973376000},"page":"857-872","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Counter Propagation Network Based Extreme Learning Machine"],"prefix":"10.1007","volume":"55","author":[{"given":"G\u00f6khan","family":"Kayhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0130smail","family":"\u0130\u015feri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"key":"11021_CR1","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s12559-019-09688-2","volume":"12","author":"X Bi","year":"2020","unstructured":"Bi X, Zhao X, Huang H et al (2020) Functional brain network classification for Alzheimer\u2019s disease detection with deep features and extreme learning machine. Cogn Comput 12:513\u2013527","journal-title":"Cogn Comput"},{"key":"11021_CR2","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s11063-012-9236-y","volume":"36","author":"J Cao","year":"2012","unstructured":"Cao J, Lin Z, Huang GB (2012) Self-adaptive evolutionary extreme learning machine. Neural Process Lett 36:285\u2013305","journal-title":"Neural Process Lett"},{"key":"11021_CR3","unstructured":"Dheeru D, Casey G (2017) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http:\/\/archive.ics.uci.edu\/ml"},{"key":"11021_CR4","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s10462-013-9405-z","volume":"44","author":"S Ding","year":"2015","unstructured":"Ding S, Zhao H, Zhang Y, Xu X, Nie R (2015) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44:103\u2013115","journal-title":"Artif Intell Rev"},{"key":"11021_CR5","doi-asserted-by":"publisher","first-page":"106433","DOI":"10.1016\/j.asoc.2020.106433","volume":"94","author":"OF Ertugrul","year":"2020","unstructured":"Ertugrul OF (2020) A novel randomized machine learning approach: reservoir computing extreme learning machine. J Appl Soft Comput 94:106433","journal-title":"J Appl Soft Comput"},{"key":"11021_CR6","doi-asserted-by":"crossref","unstructured":"Fan Q, Fan T (2021) A hybrid model of extreme learning machine based on bat and cuckoo search algorithm for regression and multiclass classification. J Math","DOI":"10.1155\/2021\/4404088"},{"key":"11021_CR7","first-page":"41","volume":"2","author":"S Haykin","year":"2004","unstructured":"Haykin S (2004) N. network: a comprehensive foundation. Neural Netw 2:41","journal-title":"Neural Netw"},{"key":"11021_CR8","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"BG Huang","year":"2006","unstructured":"Huang BG, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489\u2013501","journal-title":"Neurocomputing"},{"key":"11021_CR9","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"BG Huang","year":"2011","unstructured":"Huang BG, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2:107\u2013122","journal-title":"Int J Mach Learn Cybern"},{"key":"11021_CR10","first-page":"689","volume":"8","author":"I Iseri","year":"2014","unstructured":"Iseri I, Oz C (2014) Computer aided detection of microcalcification clusters in mammogram images with machine learning approach. Optoelectron Adv Mater Rapid Commun 8:689\u2013695","journal-title":"Optoelectron Adv Mater Rapid Commun"},{"key":"11021_CR11","doi-asserted-by":"publisher","first-page":"6601","DOI":"10.1007\/s00521-018-3735-3","volume":"35","author":"Z Jin","year":"2020","unstructured":"Jin Z, Zhou G, Gao D, Zhang Y (2020) EEG classification using sparse Bayesian extreme learning machine for brain\u2013computer interface. Neural Comput Appl 35:6601\u20136609","journal-title":"Neural Comput Appl"},{"key":"11021_CR12","doi-asserted-by":"publisher","first-page":"198730","DOI":"10.1109\/ACCESS.2020.3033455","volume":"8","author":"L Kuang","year":"2020","unstructured":"Kuang L, Shi P, Hua C, Chen B, Zhu H (2020) An enhanced extreme learning machine for dissolved oxygen prediction in wireless sensor networks. IEEE Access 8:198730","journal-title":"IEEE Access"},{"issue":"12","key":"11021_CR13","doi-asserted-by":"publisher","first-page":"4699","DOI":"10.1109\/TCSI.2019.2940642","volume":"66","author":"H-T Li","year":"2019","unstructured":"Li H-T, Chou C-Y, Chen Y-T, Wang S-H, Wu A-Y (2019) Robust and light weight ensemble extreme learning machine engine based on eigenspace domain for compressed learning. IEEE Trans Circuits Syst I Regul Pap 66(12):4699\u20134712","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"key":"11021_CR14","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/S0165-0114(97)00322-9","volume":"107","author":"DA Linkens","year":"1999","unstructured":"Linkens DA, Chen MY (1999) Input selection and partition validation for fuzzy modelling using neural network. Fuzzy Sets Syst 107:299\u2013308","journal-title":"Fuzzy Sets Syst"},{"key":"11021_CR15","doi-asserted-by":"publisher","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":"11021_CR16","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1080\/00207179408921470","volume":"60","author":"J Nie","year":"1994","unstructured":"Nie J, Linkens DA (1994) Fast self-learning multivariable fuzzy controllers constructed from a modified CPN network. Int J Control 60:369\u2013393","journal-title":"Int J Control"},{"key":"11021_CR17","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1080\/00103624.2015.1104342","volume":"47","author":"MS Odabas","year":"2016","unstructured":"Odabas MS, Kayhan G, Ergun E, Senyer N (2016) Using artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John\u2019s Wort leaves. Commun Soil Sci Plant Anal 47:237\u2013245","journal-title":"Commun Soil Sci Plant Anal"},{"key":"11021_CR18","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1109\/TCBB.2010.13","volume":"8","author":"S Saraswathi","year":"2011","unstructured":"Saraswathi S, Sundaram S, Sundararajan N, Zimmermann M, Nilsen-Hamilton M (2011) ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE\/ACM Trans Comput Biol Bioinform 8:452\u2013463","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"11021_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-9-319","volume":"9","author":"A Statnikov","year":"2008","unstructured":"Statnikov A, Wang L, Aliferis CF (2008) A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinform 9:1\u201310","journal-title":"BMC Bioinform"},{"key":"11021_CR20","doi-asserted-by":"publisher","first-page":"106639","DOI":"10.1016\/j.compeleceng.2020.106639","volume":"85","author":"MA Shehab","year":"2020","unstructured":"Shehab MA, Kahraman N (2020) A weighted voting ensemble of efficient regularized extreme learning machine. Comput Electr Eng 85:106639","journal-title":"Comput Electr Eng"},{"key":"11021_CR21","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1080\/21642583.2020.1759156","volume":"8","author":"S Song","year":"2020","unstructured":"Song S, Wang M, Lin Y (2020) An improved algorithm for incremental extreme learning machine. Syst Sci Control Eng 8:308\u2013317","journal-title":"Syst Sci Control Eng"},{"key":"11021_CR22","first-page":"1","volume":"22","author":"J Wang","year":"2021","unstructured":"Wang J, Lu S, Wang SH et al (2021) A review on extreme learning machine. Multimed Tools Appl 22:1\u201350","journal-title":"Multimed Tools Appl"},{"key":"11021_CR23","doi-asserted-by":"publisher","first-page":"105115","DOI":"10.1016\/j.compag.2019.105115","volume":"168","author":"L Wu","year":"2020","unstructured":"Wu L, Huang G, Fan J, Ma X, Zhou H, Zeng W (2020) Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Comput Electron Agric 168:105115","journal-title":"Comput Electron Agric"},{"key":"11021_CR24","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s12559-017-9451-y","volume":"9","author":"T Wu","year":"2017","unstructured":"Wu T, Yao M, Yang J (2017) Dolphin swarm extreme learning machine. Cognit Comput 9:275\u2013284","journal-title":"Cognit Comput"},{"key":"11021_CR25","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, Yang J (2014) Genetic ensemble of extreme learning machine. Neurocomputing 129:175\u2013184","journal-title":"Neurocomputing"},{"key":"11021_CR26","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.patcog.2016.04.003","volume":"58","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Wu J, Cai Z, Zhang P, Chen L (2016) Memetic extreme learning machine. Pattern Recognit 58:135\u2013148","journal-title":"Pattern Recognit"},{"key":"11021_CR27","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1016\/j.patcog.2005.03.028","volume":"38","author":"QY Zhu","year":"2005","unstructured":"Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recognit 38:1759\u20131763","journal-title":"Pattern Recognit"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11021-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11021-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11021-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T14:31:08Z","timestamp":1678113068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11021-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,12]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["11021"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11021-2","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,12]]},"assertion":[{"value":"26 August 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal ethics"}}]}}