{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:51:37Z","timestamp":1740160297279,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872220","61972226"],"award-info":[{"award-number":["61872220","61972226"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s13042-021-01391-9","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T16:06:29Z","timestamp":1628093189000},"page":"199-216","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Kernel risk-sensitive mean p-power error based robust extreme learning machine for classification"],"prefix":"10.1007","volume":"13","author":[{"given":"Liang-Rui","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying-Lian","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junliang","family":"Shang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6104-2149","authenticated-orcid":false,"given":"Jin-Xing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"1391_CR1","first-page":"44","volume":"4","author":"AHH Alasadi","year":"2017","unstructured":"Alasadi AHH, Alsafy BMR (2017) Diagnosis of malignant melanoma of skin cancer types. Int J Interact Multimed Artif Intell 4:44\u201349","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"1391_CR2","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/s11063-016-9552-8","volume":"45","author":"MZ Alom","year":"2017","unstructured":"Alom MZ, Sidike P, Taha TM, Asari VK (2017) State preserving extreme learning machine: a monotonically increasing learning approach. Neural Process Lett 45:703\u2013725","journal-title":"Neural Process Lett"},{"key":"1391_CR3","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s13042-014-0283-8","volume":"7","author":"S Balasundaram","year":"2016","unstructured":"Balasundaram S, Gupta D (2016) On optimization based extreme learning machine in primal for regression and classification by functional iterative method. Int J Mach Learn Cybern 7:707\u2013728","journal-title":"Int J Mach Learn Cybern"},{"key":"1391_CR4","doi-asserted-by":"publisher","first-page":"4528","DOI":"10.1016\/j.jfranklin.2015.07.002","volume":"352","author":"JW Cao","year":"2015","unstructured":"Cao JW, Zhao YF, Lai XP, Ong MEH, Yin C, Koh ZX, Liu N (2015) Landmark recognition with sparse representation classification and extreme learning machine. J Franklin Inst-Eng Appl Math 352:4528\u20134545. https:\/\/doi.org\/10.1016\/j.jfranklin.2015.07.002","journal-title":"J Franklin Inst-Eng Appl Math"},{"key":"1391_CR5","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.patcog.2018.02.010","volume":"79","author":"B Chen","year":"2018","unstructured":"Chen B, Wang X, Lu N, Wang S, Cao J, Qin J (2018) Mixture correntropy for robust learning. Pattern Recogn 79:318\u2013327","journal-title":"Pattern Recogn"},{"key":"1391_CR6","doi-asserted-by":"publisher","first-page":"2101","DOI":"10.1109\/TCYB.2017.2727278","volume":"48","author":"B Chen","year":"2017","unstructured":"Chen B, Xing L, Wang X, Qin J, Zheng N (2017) Robust learning with kernel mean p-power error loss. IEEE Trans Cybern 48:2101\u20132113","journal-title":"IEEE Trans Cybern"},{"key":"1391_CR7","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.dsp.2015.02.009","volume":"40","author":"B Chen","year":"2015","unstructured":"Chen B, Xing L, Wu Z, Liang J, Principe JC, Zheng N (2015) Smoothed least mean p-power error criterion for adaptive filtering. Digit Signal Process 40:154\u2013163","journal-title":"Digit Signal Process"},{"key":"1391_CR8","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.1109\/TSP.2017.2669903","volume":"65","author":"B Chen","year":"2017","unstructured":"Chen B, Xing L, Xu B, Zhao H, Zheng N, Principe JC (2017) Kernel risk-sensitive loss: definition, properties and application to robust adaptive filtering. IEEE Trans Signal Process 65:2888\u20132901","journal-title":"IEEE Trans Signal Process"},{"key":"1391_CR9","doi-asserted-by":"publisher","first-page":"3376","DOI":"10.1109\/TSP.2016.2539127","volume":"64","author":"B Chen","year":"2016","unstructured":"Chen B, Xing L, Zhao H, Zheng N, Principe JC (2016) Generalized correntropy for robust adaptive filtering. IEEE Trans Signal Process 64:3376\u20133387","journal-title":"IEEE Trans Signal Process"},{"key":"1391_CR10","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1016\/j.eswa.2010.07.014","volume":"38","author":"FL Chen","year":"2011","unstructured":"Chen FL, Ou TY (2011) Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Expert Syst Appl 38:1336\u20131345","journal-title":"Expert Syst Appl"},{"key":"1391_CR11","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.neucom.2016.12.029","volume":"230","author":"K Chen","year":"2017","unstructured":"Chen K, Lv Q, Lu Y, Dou Y (2017) Robust regularized extreme learning machine for regression using iteratively reweighted least squares. Neurocomputing 230:345\u2013358","journal-title":"Neurocomputing"},{"key":"1391_CR12","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1007\/s00521-015-1916-x","volume":"27","author":"L Chen","year":"2016","unstructured":"Chen L, Qu H, Zhao J, Chen B, Principe JC (2016) Efficient and robust deep learning with Correntropy-induced loss function. Neural Comput Appl 27:1019\u20131031","journal-title":"Neural Comput Appl"},{"key":"1391_CR13","doi-asserted-by":"crossref","unstructured":"Deng W, Zheng Q, Chen L (2009)  Regularized extreme learning machine[C]. In: 2009 IEEE symposium on computational intelligence and data mining. IEEE, pp 389\u2013395","DOI":"10.1109\/CIDM.2009.4938676"},{"key":"1391_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04044-9","author":"U Ergul","year":"2019","unstructured":"Ergul U, Bilgin G (2019) MCK-ELM: multiple composite kernel extreme learning machine for hyperspectral images. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04044-9","journal-title":"Neural Comput Appl"},{"key":"1391_CR15","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.eswa.2018.03.024","volume":"104","author":"M Eshtay","year":"2018","unstructured":"Eshtay M, Faris H, Obeid N (2018) Improving extreme learning machine by competitive swarm optimization and its application for medical diagnosis problems. Expert Syst Appl 104:134\u2013152","journal-title":"Expert Syst Appl"},{"key":"1391_CR16","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","volume":"6","author":"G-B Huang","year":"2014","unstructured":"Huang G-B (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6:376\u2013390","journal-title":"Cogn Comput"},{"key":"1391_CR17","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G-B Huang","year":"2011","unstructured":"Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42:513\u2013529","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"key":"1391_CR18","doi-asserted-by":"crossref","unstructured":"Huang GB, Zhu QY, Siew CK  (2004) Extreme learning machine: a new learning scheme of feedforward neural networks[C]. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol 2. IEEE, pp  985\u2013990","DOI":"10.1109\/IJCNN.2004.1380068"},{"key":"1391_CR19","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 70:489\u2013501","journal-title":"Neurocomputing"},{"key":"1391_CR20","doi-asserted-by":"publisher","first-page":"22645","DOI":"10.1109\/ACCESS.2018.2825978","volume":"6","author":"M Jiang","year":"2018","unstructured":"Jiang M, Cao F, Lu Y (2018) Extreme learning machine with enhanced composite feature for spectral-spatial hyperspectral image classification. IEEE Access 6:22645\u201322654","journal-title":"IEEE Access"},{"key":"1391_CR21","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.1109\/jbhi.2020.2975199","volume":"24","author":"C-N Jiao","year":"2020","unstructured":"Jiao C-N, Gao Y-L, Yu N, Liu J-X, Qi L-Y (2020) Hyper-graph regularized constrained nmf for selecting differentially expressed genes and tumor classification. IEEE J Biomed Health Inform 24:3002\u20133011. https:\/\/doi.org\/10.1109\/jbhi.2020.2975199","journal-title":"IEEE J Biomed Health Inform"},{"key":"1391_CR22","doi-asserted-by":"publisher","first-page":"5286","DOI":"10.1109\/tsp.2007.896065","volume":"55","author":"WF Liu","year":"2007","unstructured":"Liu WF, Pokharel PP, Principe JC (2007) Correntropy: properties and applications in non-gaussian signal processing. IEEE Trans Signal Process 55:5286\u20135298. https:\/\/doi.org\/10.1109\/tsp.2007.896065","journal-title":"IEEE Trans Signal Process"},{"key":"1391_CR23","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1109\/tcyb.2017.2738060","volume":"48","author":"XJ Lu","year":"2018","unstructured":"Lu XJ, Ming L, Liu WB, Li HX (2018) Probabilistic regularized extreme learning machine for robust modeling of noise data. IEEE Trans Cybern 48:2368\u20132377. https:\/\/doi.org\/10.1109\/tcyb.2017.2738060","journal-title":"IEEE Trans Cybern"},{"key":"1391_CR24","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1016\/j.future.2018.04.085","volume":"93","author":"X Luo","year":"2019","unstructured":"Luo X, Jiang C, Wang W, Xu Y, Wang J, Zhao W (2019) User behavior prediction in social networks using weighted extreme learning machine with distribution optimization. Futur Gener Comput Syst 93:1023\u20131035","journal-title":"Futur Gener Comput Syst"},{"key":"1391_CR25","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s13042-019-00967-w","volume":"11","author":"X Luo","year":"2020","unstructured":"Luo X, Li Y, Wang W, Ban X, Wang J, Zhao W (2020) A robust multilayer extreme learning machine using kernel risk-sensitive loss criterion. Int J Mach Learn Cybern 11:197\u2013216","journal-title":"Int J Mach Learn Cybern"},{"key":"1391_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-020-03790-1","author":"L-R Ren","year":"2020","unstructured":"Ren L-R, Gao Y-L, Liu J-X, Shang J, Zheng C-H (2020) Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification. BMC Bioinformatics. https:\/\/doi.org\/10.1186\/s12859-020-03790-1","journal-title":"BMC Bioinformatics"},{"key":"1391_CR27","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neucom.2018.05.100","volume":"313","author":"Z Ren","year":"2018","unstructured":"Ren Z, Yang L (2018) Correntropy-based robust extreme learning machine for classification. Neurocomputing 313:74\u201384","journal-title":"Neurocomputing"},{"key":"1391_CR28","doi-asserted-by":"crossref","unstructured":"Ri JH, Tian G, Liu Y et al (2020) Extreme learning machine with hybrid cost function of G-mean and probability for imbalance learning[J]. Int J Mach Learn Cybern 11(9):2007\u20132020","DOI":"10.1007\/s13042-020-01090-x"},{"key":"1391_CR29","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","volume":"27","author":"J Tang","year":"2016","unstructured":"Tang J, Deng C, Huang G (2016) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Networks 27:809\u2013821","journal-title":"IEEE Trans Neural Networks"},{"key":"1391_CR30","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1007\/s10489-018-1322-z","volume":"49","author":"Y Wang","year":"2019","unstructured":"Wang Y, Wang A, Ai Q, Sun H (2019) Ensemble based fuzzy weighted extreme learning machine for gene expression classification. Appl Intell 49:1161\u20131171","journal-title":"Appl Intell"},{"key":"1391_CR31","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.cmpb.2017.06.005","volume":"147","author":"J Xia","year":"2017","unstructured":"Xia J et al (2017) Ultrasound-based differentiation of malignant and benign thyroid Nodules: an extreme learning machine approach. Comput Methods Programs Biomed 147:37\u201349","journal-title":"Comput Methods Programs Biomed"},{"key":"1391_CR32","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.1007\/s00521-012-1184-y","volume":"23","author":"H Xing","year":"2013","unstructured":"Xing H, Wang X (2013) Training extreme learning machine via regularized correntropy criterion. Neural Comput Appl 23:1977\u20131986","journal-title":"Neural Comput Appl"},{"key":"1391_CR33","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neunet.2019.09.030","volume":"122","author":"J Yang","year":"2020","unstructured":"Yang J, Cao JW, Wang TL, Xue AK, Chen BD (2020) Regularized correntropy criterion based semi-supervised ELM. Neural Netw 122:117\u2013129. https:\/\/doi.org\/10.1016\/j.neunet.2019.09.030","journal-title":"Neural Netw"},{"key":"1391_CR34","first-page":"1","volume":"2018","author":"Z Yuan","year":"2018","unstructured":"Yuan Z, Wang X, Cao J, Zhao H, Chen B (2018) Robust matching pursuit extreme learning machines. Sci Progr 2018:1\u201310","journal-title":"Sci Progr"},{"key":"1391_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-020-01114-6","author":"HG Zhang","year":"2020","unstructured":"Zhang HG, Yang JF, Jia GM, Han SC, Zhou XR (2020) ELM-MC: multi-label classification framework based on extreme learning machine. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-020-01114-6","journal-title":"Int J Mach Learn Cybern"},{"key":"1391_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 M (2015) Outlier-robust extreme learning machine for regression problems. Neurocomputing 151:1519\u20131527","journal-title":"Neurocomputing"},{"key":"1391_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/e21060588","author":"T Zhang","year":"2019","unstructured":"Zhang T, Wang SY, Zhang HN, Xiong K, Wang L (2019) Kernel risk-sensitive mean p-power error algorithms for robust learning. Entropy. https:\/\/doi.org\/10.3390\/e21060588","journal-title":"Entropy"},{"key":"1391_CR38","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.eswa.2017.12.015","volume":"96","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Wang Y, Zhou G, Jin J, Wang B, Wang X, Cichocki A (2018) Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Expert Syst Appl 96:302\u2013310","journal-title":"Expert Syst Appl"},{"key":"1391_CR39","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.ast.2019.04.023","volume":"89","author":"Y-P Zhao","year":"2019","unstructured":"Zhao Y-P, Tan J-F, Wang J-J, Yang Z (2019) C-loss based extreme learning machine for estimating power of small-scale turbojet engine. Aerosp Sci Technol 89:407\u2013419","journal-title":"Aerosp Sci Technol"},{"key":"1391_CR40","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.1109\/tcyb.2014.2363492","volume":"45","author":"HM Zhou","year":"2015","unstructured":"Zhou HM, Huang GB, Lin ZP, Wang H, Soh YC (2015) Stacked extreme learning machines. IEEE Trans Cybern 45:2013\u20132025. https:\/\/doi.org\/10.1109\/tcyb.2014.2363492","journal-title":"IEEE Trans Cybern"},{"key":"1391_CR41","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.neucom.2016.11.021","volume":"225","author":"H Zhu","year":"2017","unstructured":"Zhu H, Tsang ECC, Wang X, Ashfaq RAR (2017) Monotonic classification extreme learning machine. Neurocomputing 225:205\u2013213","journal-title":"Neurocomputing"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01391-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01391-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01391-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T09:17:55Z","timestamp":1641460675000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01391-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,4]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1391"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01391-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2021,8,4]]},"assertion":[{"value":"17 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2021","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"}}]}}