{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T20:10:38Z","timestamp":1698523838806},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"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":["Neural Process Lett"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s11063-020-10282-z","type":"journal-article","created":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T12:02:53Z","timestamp":1592395373000},"page":"1927-1944","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Parameter-Free Extreme Learning Machine for Imbalanced Classification"],"prefix":"10.1007","volume":"52","author":[{"given":"Li","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiyi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruizhi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangzhang","family":"Gan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"10282_CR1","unstructured":"Longadge R, Dongre S (2013) Class imbalance problem in data mining review. arXiv:13051707"},{"issue":"4","key":"10282_CR2","first-page":"42","volume":"2","author":"V Ganganwar","year":"2012","unstructured":"Ganganwar V (2012) An overview of classification algorithms for imbalanced datasets. Int J Emerg Technol Adv Eng 2(4):42\u201347","journal-title":"Int J Emerg Technol Adv Eng"},{"issue":"23","key":"10282_CR3","doi-asserted-by":"publisher","first-page":"33375","DOI":"10.1007\/s11042-019-7543-2","volume":"78","author":"L Li","year":"2019","unstructured":"Li L, Sun R, Cai S, Zhao K, Zhang Q (2019) A review of improved extreme learning machine methods for data stream classification. Multimed Tools Appl 78(23):33375\u201333400","journal-title":"Multimed Tools Appl"},{"key":"10282_CR4","doi-asserted-by":"crossref","unstructured":"Srimuang W, Intarasothonchun S (2015) Classification model of network intrusion using weighted extreme learning machine. In: 2015 12th international joint conference on computer science and software engineering (JCSSE), 2015. IEEE, pp 190\u2013194","DOI":"10.1109\/JCSSE.2015.7219794"},{"issue":"4","key":"10282_CR5","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s11280-012-0178-0","volume":"16","author":"W Wei","year":"2013","unstructured":"Wei W, Li J, Cao L, Ou Y, Chen J (2013) Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16(4):449\u2013475","journal-title":"World Wide Web"},{"key":"10282_CR6","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, Bing G (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220\u2013239","journal-title":"Expert Syst Appl"},{"key":"10282_CR7","doi-asserted-by":"crossref","unstructured":"Burnaev E, Erofeev P, Papanov A (2015) Influence of resampling on accuracy of imbalanced classification. In: Eighth international conference on machine vision (ICMV 2015), 2015. International Society for Optics and Photonics, p 987521","DOI":"10.1117\/12.2228523"},{"key":"10282_CR8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2014.08.091","volume":"163","author":"F Charte","year":"2015","unstructured":"Charte F, Rivera AJ, del Jesus MJ, Herrera F (2015) Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing 163:3\u201316","journal-title":"Neurocomputing"},{"issue":"5","key":"10282_CR9","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","volume":"27","author":"S Wang","year":"2014","unstructured":"Wang S, Minku LL, Yao X (2014) Resampling-based ensemble methods for online class imbalance learning. IEEE Trans Knowl Data Eng 27(5):1356\u20131368","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10282_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2956530","author":"X Zhu","year":"2019","unstructured":"Zhu X, Yang J, Zhang C, Zhang S (2019) Efficient utilization of missing data in cost-sensitive learning. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2019.2956530","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10282_CR11","doi-asserted-by":"crossref","unstructured":"Zheng E, Zhang C, Liu X, Lu H, Sun J (2013) Cost-sensitive extreme learning machine. In: International conference on advanced data mining and applications, 2013. Springer, pp 478\u2013488","DOI":"10.1007\/978-3-642-53917-6_43"},{"key":"10282_CR12","doi-asserted-by":"crossref","unstructured":"Wang X, Liu X, Japkowicz N, Matwin S (2013) Resampling and cost-sensitive methods for imbalanced multi-instance learning. In: 2013 IEEE 13th international conference on data mining workshops, 2013. IEEE, pp 808\u2013816","DOI":"10.1109\/ICDMW.2013.85"},{"key":"10282_CR13","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neucom.2014.06.021","volume":"143","author":"Y Qian","year":"2014","unstructured":"Qian Y, Liang Y, Li M, Feng G, Shi X (2014) A resampling ensemble algorithm for classification of imbalance problems. Neurocomputing 143:57\u201367","journal-title":"Neurocomputing"},{"key":"10282_CR14","doi-asserted-by":"publisher","first-page":"107175","DOI":"10.1016\/j.patcog.2019.107175","volume":"105","author":"X Zhu","year":"2019","unstructured":"Zhu X, Zhu Y, Zheng W (2019) Spectral rotation for deep one-step clustering. Pattern Recognit 105:107175","journal-title":"Pattern Recognit"},{"issue":"10","key":"10282_CR15","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1109\/TKDE.2018.2873378","volume":"31","author":"X Zhu","year":"2018","unstructured":"Zhu X, Zhang S, He W, Hu R, Lei C, Zhu P (2018) One-step multi-view spectral clustering. IEEE Trans Knowl Data Eng 31(10):2022\u20132034","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10282_CR16","unstructured":"Ling CX, Sheng VS (2008) Cost-sensitive learning and the class imbalance problem. In: Encyclopedia of machine learning, vol 2011, pp 231\u2013235"},{"key":"10282_CR17","doi-asserted-by":"crossref","unstructured":"Ren Y, Zhao P, Xu Z, Yao D (2017) Balanced self-paced learning with feature corruption. In: 2017 international joint conference on neural networks (IJCNN), 2017. IEEE, pp 2064\u20132071","DOI":"10.1109\/IJCNN.2017.7966104"},{"issue":"1\u20133","key":"10282_CR18","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\u20133):489\u2013501","journal-title":"Neurocomputing"},{"key":"10282_CR19","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, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32\u201348. https:\/\/doi.org\/10.1016\/j.neunet.2014.10.001","journal-title":"Neural Netw"},{"key":"10282_CR20","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, 2017. Springer, pp 885\u2013895","DOI":"10.1007\/978-3-319-59427-9_91"},{"key":"10282_CR21","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","journal-title":"Neurocomputing"},{"issue":"8","key":"10282_CR22","doi-asserted-by":"publisher","first-page":"142","DOI":"10.3390\/sym9080142","volume":"9","author":"Y Akbulut","year":"2017","unstructured":"Akbulut Y, \u015eeng\u00fcr A, Guo Y, Smarandache F (2017) A novel neutrosophic weighted extreme learning machine for imbalanced data set. Symmetry 9(8):142","journal-title":"Symmetry"},{"issue":"3","key":"10282_CR23","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1007\/s10489-014-0610-5","volume":"42","author":"X Zhang","year":"2015","unstructured":"Zhang X, Song Q, Wang G, Zhang K, He L, Jia X (2015) A dissimilarity-based imbalance data classification algorithm. Appl Intell 42(3):544\u2013565","journal-title":"Appl Intell"},{"issue":"1","key":"10282_CR24","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s12293-017-0236-3","volume":"11","author":"C Lu","year":"2019","unstructured":"Lu C, Ke H, Zhang G, Mei Y, Xu H (2019) An improved weighted extreme learning machine for imbalanced data classification. Memetic Comput 11(1):27\u201334","journal-title":"Memetic Comput"},{"key":"10282_CR25","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neucom.2013.05.051","volume":"128","author":"K Li","year":"2014","unstructured":"Li K, Kong X, Lu Z, Wenyin L, Yin J (2014) Boosting weighted ELM for imbalanced learning. Neurocomputing 128:15\u201321","journal-title":"Neurocomputing"},{"key":"10282_CR26","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1016\/j.asoc.2018.10.011","volume":"73","author":"BS Raghuwanshi","year":"2018","unstructured":"Raghuwanshi BS, Shukla S (2018) Class-specific kernelized extreme learning machine for binary class imbalance learning. Appl Soft Comput 73:1026\u20131038","journal-title":"Appl Soft Comput"},{"key":"10282_CR27","first-page":"1","volume":"23","author":"R Hu","year":"2019","unstructured":"Hu R, Zhu X, Zhu Y, Gan J (2019) Robust SVM with adaptive graph learning. World Wide Web 23:1\u201324","journal-title":"World Wide Web"},{"key":"10282_CR28","first-page":"1","volume":"23","author":"X Zhu","year":"2019","unstructured":"Zhu X, Gan J, Lu G, Li J, Zhang S (2019) Spectral clustering via half-quadratic optimization. World Wide Web 23:1\u201320","journal-title":"World Wide Web"},{"key":"10282_CR29","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, Zhang S, Yang W (2017) Class-specific cost regulation extreme learning machine for imbalanced classification. Neurocomputing 261:70\u201382","journal-title":"Neurocomputing"},{"key":"10282_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.08.029","author":"J Gan","year":"2018","unstructured":"Gan J, Wen G, Yu H, Zheng W, Lei C (2018) Supervised feature selection by self-paced learning regression. Pattern Recogn Lett. https:\/\/doi.org\/10.1016\/j.patrec.2018.08.029","journal-title":"Pattern Recogn Lett"},{"issue":"9","key":"10282_CR31","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1109\/TMM.2017.2703636","volume":"19","author":"X Zhu","year":"2017","unstructured":"Zhu X, Li X, Zhang S, Xu Z, Yu L, Wang C (2017) Graph PCA hashing for similarity search. IEEE Trans Multimed 19(9):2033\u20132044","journal-title":"IEEE Trans Multimed"},{"key":"10282_CR32","unstructured":"Zheng W, Zhu X, Wen G, Zhu Y, Yu H, Gan J (2018) Unsupervised feature selection by self-paced learning regularization. Pattern Recognit Lett"},{"key":"10282_CR33","doi-asserted-by":"crossref","unstructured":"Huang C, Li Y, Change Loy C, Tang X (2016) Learning deep representation for imbalanced classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. pp 5375\u20135384","DOI":"10.1109\/CVPR.2016.580"},{"issue":"1","key":"10282_CR34","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ins.2011.09.015","volume":"185","author":"J Cao","year":"2012","unstructured":"Cao J, Lin Z, Huang G-B, Liu N (2012) Voting based extreme learning machine. Inf Sci 185(1):66\u201377","journal-title":"Inf Sci"},{"issue":"6","key":"10282_CR35","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1007\/s12559-018-9602-9","volume":"10","author":"T Wang","year":"2018","unstructured":"Wang T, Cao J, Lai X, Chen B (2018) Deep weighted extreme learning machine. Cognit Comput 10(6):890\u2013907","journal-title":"Cognit Comput"},{"key":"10282_CR36","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"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10282-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-020-10282-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10282-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T23:28:00Z","timestamp":1623886080000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-020-10282-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,17]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["10282"],"URL":"https:\/\/doi.org\/10.1007\/s11063-020-10282-z","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,17]]},"assertion":[{"value":"17 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}