{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:34:05Z","timestamp":1774276445211,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"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":["Appl Intell"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s10489-022-03237-5","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T11:03:58Z","timestamp":1648551838000},"page":"16940-16961","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Pinball loss support vector data description for outlier detection"],"prefix":"10.1007","volume":"52","author":[{"given":"Guangzheng","family":"Zhong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanshan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangjun","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"3237_CR1","unstructured":"David MJ, TaxRobert P, Duin W Support vector data description, Machine Learning"},{"key":"3237_CR2","volume-title":"Learning with kernels: support vector machines, regularization, optimization, and beyond","author":"S Bernhard","year":"2003","unstructured":"Bernhard S (2003) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge"},{"issue":"4","key":"3237_CR3","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1109\/TNN.2006.875968","volume":"17","author":"A Navia-Vazquez","year":"2006","unstructured":"Navia-Vazquez A, Gutierrez-Gonzalez D, Parrado-Hernandez E, Navarro-Abellan JJ (2006) Distributed support vector machines. IEEE Trans Neural Netw 17(4):1091\u20131097","journal-title":"IEEE Trans Neural Netw"},{"key":"3237_CR4","doi-asserted-by":"crossref","unstructured":"Chandola V, Banerjee A, Kumar V Anomaly detection: A survey, ACM Computing Surveys 41 (3)","DOI":"10.1145\/1541880.1541882"},{"key":"3237_CR5","doi-asserted-by":"crossref","unstructured":"Li H, Yuan Y, Fan Z-P, Liu Y (2014) A fta-based method for risk decision-making in emergency response, 42, 49\u201357","DOI":"10.1016\/j.cor.2012.08.015"},{"key":"3237_CR6","unstructured":"Giacinto G, Perdisci R, Rio MD, Roli F Intrusion detection in computer networks by a modular ensemble of one-class classifiers, Information Fusion"},{"key":"3237_CR7","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TNN.2006.884673","volume":"18","author":"K Lee","year":"2007","unstructured":"Lee K, Kim DW, Lee KH, Lee D (2007) Density-induced support vector data description. IEEE Trans Neural Netw 18:284\u2013 289","journal-title":"IEEE Trans Neural Netw"},{"key":"3237_CR8","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Yu PS (2008) Outlier detection with uncertain data. In: DBLP","DOI":"10.1137\/1.9781611972788.44"},{"key":"3237_CR9","unstructured":"Bi J, Zhang T (2004) Support vector classification with input data uncertainty. 17"},{"issue":"7","key":"3237_CR10","doi-asserted-by":"publisher","first-page":"3343","DOI":"10.1016\/j.eswa.2013.11.025","volume":"41","author":"M Cha","year":"2014","unstructured":"Cha M, Kim JS, Baek JG (2014) Density weighted support vector data description. Expert Syst Appl 41(7):3343\u20133350","journal-title":"Expert Syst Appl"},{"issue":"DEC","key":"3237_CR11","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.knosys.2015.09.025","volume":"90","author":"G Chen","year":"2015","unstructured":"Chen G, Zhang X, Wang ZJ, Li F (2015) Robust support vector data description for outlier detection with noise or uncertain data. Knowl-Based Syst 90(DEC):129\u2013137","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"3237_CR12","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/s10115-012-0484-y","volume":"34","author":"B Liu","year":"2013","unstructured":"Liu B, Xiao Y, Cao L, Hao Z, Deng F (2013) Svdd-based outlier detection on uncertain data. Knowledge Inf Syst 34(3):597\u2013 618","journal-title":"Knowledge Inf Syst"},{"issue":"3","key":"3237_CR13","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1016\/j.patcog.2012.09.018","volume":"46","author":"CD Wang","year":"2013","unstructured":"Wang CD, Lai JH (2013) Position regularized support vector domain description. Pattern Recogn 46(3):875\u2013884","journal-title":"Pattern Recogn"},{"issue":"7","key":"3237_CR14","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1109\/TKDE.2013.108","volume":"26","author":"B Liu","year":"2014","unstructured":"Liu B, Xiao Y, Yu PS, Hao Z, Cao L (2014) An efficient approach for outlier detection with imperfect data labels. Knowledge & Data Engineering IEEE Transactions on 26(7):1602\u20131616","journal-title":"Knowledge & Data Engineering IEEE Transactions on"},{"issue":"8","key":"3237_CR15","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.1109\/TNNLS.2019.2935975","volume":"31","author":"T Ergen","year":"2020","unstructured":"Ergen T, Kozat SS (2020) Unsupervised anomaly detection with lstm neural networks. IEEE Trans Neural Netw Learn Syst 31(8):3127\u20133141. https:\/\/doi.org\/10.1109\/TNNLS.2019.2935975","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"3237_CR16","doi-asserted-by":"publisher","first-page":"3994","DOI":"10.1109\/TNNLS.2017.2737941","volume":"29","author":"N G\u00f6rnitz","year":"2018","unstructured":"G\u00f6rnitz N, Lima LA, M\u00fcller K-R, Kloft M, Nakajima S (2018) Support vector data descriptions and k -means clustering: One class? IEEE Trans Neural Netw Learn Syst 29(9):3994\u20134006. https:\/\/doi.org\/10.1109\/TNNLS.2017.2737941","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"3237_CR17","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1109\/TASE.2013.2285571","volume":"11","author":"W Du","year":"2014","unstructured":"Du W, Tian Y, Qian F (2014) Monitoring for nonlinear multiple modes process based on ll-svdd-mrda. IEEE Trans Autom Sci Eng 11(4):1133\u20131148. https:\/\/doi.org\/10.1109\/TASE.2013.2285571","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"3237_CR18","doi-asserted-by":"publisher","first-page":"107119","DOI":"10.1016\/j.patcog.2019.107119","volume":"100","author":"M Turkoz","year":"2019","unstructured":"Turkoz M, Kim S, Son Y, Jeong MK, Elsayed EA (2019) Generalized support vector data description for anomaly detection. Pattern Recog 100:107119","journal-title":"Pattern Recog"},{"key":"3237_CR19","doi-asserted-by":"crossref","unstructured":"Koenker R, Hall R, Jones C, Roberts J, Samuelson L, Lothgren M, Tambour M, Diewert E, Theil H, Thirlwall AP (2004) Quantile regression for longitudinal data, Academic Press Inc.","DOI":"10.1016\/j.jmva.2004.05.006"},{"key":"3237_CR20","unstructured":"Christmann A, Steinwart I (2008) How svms can estimate quantiles and the median. In: Platt J, Koller D, Singer Y, Roweis S (eds) Advances in neural information processing systems, vol 20. Curran Associates, Inc"},{"issue":"1","key":"3237_CR21","doi-asserted-by":"publisher","first-page":"211","DOI":"10.3150\/10-BEJ267","volume":"17","author":"I Steinwart","year":"2011","unstructured":"Steinwart I, Christmann A (2011) Estimating conditional quantiles with the help of the pinball loss. Bernoulli 17(1):211\u2013225. https:\/\/doi.org\/10.3150\/10-BEJ267","journal-title":"Bernoulli"},{"issue":"5","key":"3237_CR22","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1109\/TPAMI.2013.178","volume":"36","author":"X Huang","year":"2014","unstructured":"Huang X, Shi L, Suykens JAK (2014) Support vector machine classifier with pinball loss. IEEE Trans Pattern Anal Mach Intell 36(5):984\u2013997","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"3237_CR23","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/TNNLS.2015.2513006","volume":"28","author":"Y Xu","year":"2017","unstructured":"Xu Y, Yang Z, Pan X (2017) A novel twin support-vector machine with pinball loss. IEEE Trans Neural Netw Learn Syst 28(2):359\u2013370","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"Mar.1","key":"3237_CR24","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.knosys.2015.12.005","volume":"95","author":"Y Xu","year":"2016","unstructured":"Xu Y, Yang Z, Zhang Y, Pan X, Wang L (2016) A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification. Knowledge Based Systems 95(Mar.1):75\u201385","journal-title":"Knowledge Based Systems"},{"issue":"1","key":"3237_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-017-0961-9","volume":"48","author":"Y Xu","year":"2018","unstructured":"Xu Y, Wang Q, Pang X, Tian Y (2018) Maximum margin of twin spheres machine with pinball loss for imbalanced data classification. Appl Intell 48(1):1\u201312","journal-title":"Appl Intell"},{"key":"3237_CR26","doi-asserted-by":"crossref","unstructured":"Gong R, Wu C, Chu M, Wang H (2016) Twin pinball loss support vector hyper-sphere classifier for pattern recognition. In: Control & decision conference","DOI":"10.1109\/CCDC.2016.7532177"},{"key":"3237_CR27","doi-asserted-by":"publisher","first-page":"103554","DOI":"10.1016\/j.engappai.2020.103554","volume":"91","author":"K Wang","year":"2020","unstructured":"Wang K, Lan H (2020) Robust support vector data description for novelty detection with contaminated data. Eng Appl Artif Intell 91:103554","journal-title":"Eng Appl Artif Intell"},{"key":"3237_CR28","doi-asserted-by":"crossref","unstructured":"Liu B, Yin J, Xiao Y, Cao L, Yu PS (2011) Exploiting local data uncertainty to boost global outlier detection. In: IEEE International conference on data mining","DOI":"10.1109\/ICDM.2010.10"},{"key":"3237_CR29","doi-asserted-by":"crossref","unstructured":"Lazarevic A, Kumar V (2005) Feature bagging for outlier detection. In: Eleventh acm sigkdd international conference on knowledge discovery in data mining","DOI":"10.1145\/1081870.1081891"},{"issue":"11","key":"3237_CR30","doi-asserted-by":"publisher","first-page":"2088","DOI":"10.1109\/TPAMI.2009.24","volume":"31","author":"M Wu","year":"2009","unstructured":"Wu M, Ye J (2009) A small sphere and large margin approach for novelty detection using training data with outliers. IEEE Trans Pattern Anal Mach Intell 31(11):2088\u20132092","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3237_CR31","doi-asserted-by":"crossref","unstructured":"Quionero-Candela J, Sugiyama M, Schwaighofer A, Lawrence ND (2009) Dataset shift in machine learning the. MIT Press","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"3237_CR32","unstructured":"Michael S (2008) Handbook of parametric and nonparametric statistical procedures (4th ed.)., Am Stat"},{"issue":"1","key":"3237_CR33","first-page":"1","volume":"7","author":"J Demiar","year":"2006","unstructured":"Demiar J, Schuurmans D (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1\u201330","journal-title":"J Mach Learn Res"},{"issue":"200","key":"3237_CR34","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675\u2013701","journal-title":"J Am Stat Assoc"},{"key":"3237_CR35","unstructured":"Nemenyi PB (1963) Distribution-free multiple comparisons. Princeton University"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03237-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03237-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03237-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T19:51:20Z","timestamp":1668023480000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03237-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,29]]},"references-count":35,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["3237"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03237-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,29]]},"assertion":[{"value":"11 January 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}