{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:06:34Z","timestamp":1759385194752,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T00:00:00Z","timestamp":1561334400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T00:00:00Z","timestamp":1561334400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51634002"],"award-info":[{"award-number":["51634002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s00521-019-04307-5","type":"journal-article","created":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T23:46:12Z","timestamp":1561419972000},"page":"8047-8063","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Detecting outliers in industrial systems using a hybrid ensemble scheme"],"prefix":"10.1007","volume":"32","author":[{"given":"Biao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhizhong","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,24]]},"reference":[{"issue":"4","key":"4307_CR1","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1007\/s00521-014-1750-6","volume":"26","author":"Z Wang","year":"2015","unstructured":"Wang Z et al (2015) Incremental multiple instance outlier detection. Neural Comput Appl 26(4):957\u2013968","journal-title":"Neural Comput Appl"},{"issue":"3","key":"4307_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1\u201358","journal-title":"ACM Comput Surv"},{"issue":"6","key":"4307_CR3","first-page":"1","volume":"62","author":"PY Chen","year":"2014","unstructured":"Chen PY, Yang S, Mccann JA (2014) Distributed real-time anomaly detection in networked industrial sensing systems. IEEE Trans Ind Electron 62(6):1\u20131","journal-title":"IEEE Trans Ind Electron"},{"issue":"5","key":"4307_CR4","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1177\/0142331210397571","volume":"34","author":"F Liu","year":"2012","unstructured":"Liu F, Mao Z, Su W (2012) Outlier detection for process control data based on a non-linear auto-regression hidden Markov model method. Trans Inst Meas Control 34(5):527\u2013538","journal-title":"Trans Inst Meas Control"},{"key":"4307_CR5","first-page":"61","volume-title":"Towards learning normality for anomaly detection in industrial control networks","author":"F Schuster","year":"2013","unstructured":"Schuster F, Paul A, K\u00f6nig H (2013) Towards learning normality for anomaly detection in industrial control networks. Springer, Berlin, pp 61\u201372"},{"issue":"3","key":"4307_CR6","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.ins.2013.05.018","volume":"259","author":"J Zhao","year":"2014","unstructured":"Zhao J et al (2014) Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry. Inf Sci 259(3):335\u2013345","journal-title":"Inf Sci"},{"issue":"5","key":"4307_CR7","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1109\/TNNLS.2013.2283456","volume":"25","author":"H Ferdowsi","year":"2014","unstructured":"Ferdowsi H, Jagannathan S, Zawodniok M (2014) An online outlier identification and removal scheme for improving fault detection performance. IEEE Trans Neural Netw Learn Syst 25(5):908\u2013919","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4307_CR8","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1016\/j.chaos.2017.07.018","volume":"103","author":"B Wang","year":"2017","unstructured":"Wang B, Mao Z, Huang K (2017) Detecting outliers in complex nonlinear systems controlled by predictive control strategy. Chaos Solitons Fractals 103:588\u2013595","journal-title":"Chaos Solitons Fractals"},{"issue":"3\u20134","key":"4307_CR9","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1177\/0020294018771097","volume":"51","author":"B Wang","year":"2018","unstructured":"Wang B, Mao Z (2018) Detecting outliers in electric arc furnace under the condition of unlabeled, imbalanced, non-stationary and noisy data. Meas Control 51(3\u20134):83\u201393","journal-title":"Meas Control"},{"issue":"12","key":"4307_CR10","doi-asserted-by":"publisher","first-page":"3466","DOI":"10.1177\/0142331217724508","volume":"40","author":"B Wang","year":"2017","unstructured":"Wang B, Mao Z (2017) One-class classifiers ensemble based anomaly detection scheme for process control systems. Trans Inst Meas Control 40(12):3466\u20133476","journal-title":"Trans Inst Meas Control"},{"issue":"2","key":"4307_CR11","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00521-007-0169-8","volume":"18","author":"GG Cabral","year":"2009","unstructured":"Cabral GG, Oliveira ALI, Cah\u00fa CBG (2009) Combining nearest neighbor data description and structural risk minimization for one-class classification. Neural Comput Appl 18(2):175\u2013183","journal-title":"Neural Comput Appl"},{"key":"4307_CR12","first-page":"1","volume":"3","author":"J Wang","year":"2017","unstructured":"Wang J et al (2017) Dynamic hypersphere SVDD without describing boundary for one-class classification. Neural Comput Appl 3:1\u201311","journal-title":"Neural Comput Appl"},{"issue":"1","key":"4307_CR13","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/S0888-613X(00)88942-2","volume":"20","author":"O Cord\u00f3n","year":"1999","unstructured":"Cord\u00f3n O, Jesus MJD, Herrera F (1999) A proposal on reasoning methods in fuzzy rule-based classification systems. Int J Approx Reason 20(1):21\u201345","journal-title":"Int J Approx Reason"},{"issue":"7","key":"4307_CR14","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/0031-3203(90)90098-6","volume":"23","author":"S Shlien","year":"1990","unstructured":"Shlien S (1990) Multiple binary decision tree classifiers. Pattern Recognit 23(7):757\u2013763","journal-title":"Pattern Recognit"},{"issue":"3","key":"4307_CR15","first-page":"321","volume":"2","author":"DS Broomhead","year":"1988","unstructured":"Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2(3):321\u2013355","journal-title":"Complex Syst"},{"issue":"3","key":"4307_CR16","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.ins.2003.09.025","volume":"165","author":"VM Rivas","year":"2004","unstructured":"Rivas VM et al (2004) Evolving RBF neural networks for time-series forecasting with EvRBF. Inf Sci 165(3):207\u2013220","journal-title":"Inf Sci"},{"issue":"3","key":"4307_CR17","first-page":"273","volume":"20","author":"V Vapnik","year":"1995","unstructured":"Vapnik V, Cortes C (1995) Support vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"5","key":"4307_CR18","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1162\/089976600300015565","volume":"12","author":"B Scholkopf","year":"2000","unstructured":"Scholkopf B et al (2000) New support vector algorithms. Neural Comput 12(5):1207\u20131245","journal-title":"Neural Comput"},{"issue":"4","key":"4307_CR19","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s00521-010-0458-5","volume":"21","author":"MP Sesmero","year":"2012","unstructured":"Sesmero MP et al (2012) A new artificial neural network ensemble based on feature selection and class recoding. Neural Comput Appl 21(4):771\u2013783","journal-title":"Neural Comput Appl"},{"issue":"2","key":"4307_CR20","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s00521-010-0349-9","volume":"20","author":"J Tian","year":"2011","unstructured":"Tian J, Gu H, Liu W (2011) Imbalanced classification using support vector machine ensemble. Neural Comput Appl 20(2):203\u2013209","journal-title":"Neural Comput Appl"},{"key":"4307_CR21","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.ins.2016.03.043","volume":"357","author":"S Ge","year":"2016","unstructured":"Ge S et al (2016) Dynamic Clustering Forest: an ensemble framework to efficiently classify textual data stream with concept drift. Inf Sci 357:125\u2013143","journal-title":"Inf Sci"},{"issue":"2","key":"4307_CR22","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"issue":"1","key":"4307_CR23","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"8","key":"4307_CR24","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832\u2013844","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"4307_CR25","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241\u2013259","journal-title":"Neural Netw"},{"issue":"1","key":"4307_CR26","first-page":"139","volume":"2","author":"LM Manevitz","year":"2001","unstructured":"Manevitz LM, Yousef M (2001) One-class SVMs for document classification. J Mach Learn Res 2(1):139\u2013154","journal-title":"J Mach Learn Res"},{"issue":"1","key":"4307_CR27","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321\u2013357","journal-title":"J Artif Intell Res"},{"key":"4307_CR28","volume-title":"One-class classification (concept-learning in the absence of counter-examples)","author":"DMJ Tax","year":"2001","unstructured":"Tax DMJ (2001) One-class classification (concept-learning in the absence of counter-examples). Delft University of Technology, Delft"},{"issue":"10","key":"4307_CR29","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TNN.2011.2161999","volume":"22","author":"Z Haijun","year":"2011","unstructured":"Haijun Z et al (2011) Textual and visual content-based anti-phishing: a Bayesian approach. IEEE Trans Neural Netw 22(10):1532\u20131546","journal-title":"IEEE Trans Neural Netw"},{"key":"4307_CR30","doi-asserted-by":"crossref","unstructured":"Gao J, Tan PN (2006) Converting output scores from outlier detection algorithms into probability estimates. In: Sixth international conference on data mining","DOI":"10.1109\/ICDM.2006.43"},{"issue":"1","key":"4307_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39(1):1\u201338","journal-title":"J R Stat Soc"},{"key":"4307_CR32","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.neucom.2015.03.051","volume":"166","author":"E Parhizkar","year":"2015","unstructured":"Parhizkar E, Abadi M (2015) BeeOWA: a novel approach based on ABC algorithm and induced OWA operators for constructing one-class classifier ensembles. Neurocomputing 166:367\u2013381","journal-title":"Neurocomputing"},{"issue":"3","key":"4307_CR33","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1109\/TSMCA.2010.2084081","volume":"41","author":"TM Khoshgoftaar","year":"2011","unstructured":"Khoshgoftaar TM, Van Hulse J, Napolitano A (2011) Comparing boosting and bagging techniques with noisy and imbalanced data. IEEE Trans Syst Man Cybern A Syst Hum 41(3):552\u2013568","journal-title":"IEEE Trans Syst Man Cybern A Syst Hum"},{"issue":"9","key":"4307_CR34","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"4307_CR35","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1\u201330","journal-title":"J Mach Learn Res"},{"issue":"4","key":"4307_CR36","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neucom.2011.10.020","volume":"82","author":"L Li","year":"2012","unstructured":"Li L, Mao Z (2012) A direct adaptive controller for EAF electrode regulator system using neural networks. Neurocomputing 82(4):91\u201398","journal-title":"Neurocomputing"},{"issue":"5","key":"4307_CR37","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0959-1524(02)00068-9","volume":"13","author":"LH Chiang","year":"2003","unstructured":"Chiang LH, Pell RJ, Seasholtz MB (2003) Exploring process data with the use of robust outlier detection algorithms. J Process Control 13(5):437\u2013449","journal-title":"J Process Control"},{"issue":"7","key":"4307_CR38","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2014","unstructured":"Sch\u00f6lkopf B et al (2014) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443\u20131471","journal-title":"Neural Comput"},{"key":"4307_CR39","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural networks for pattern recognition","author":"CM Bishop","year":"1995","unstructured":"Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford"},{"issue":"4","key":"4307_CR40","doi-asserted-by":"publisher","first-page":"3167","DOI":"10.1109\/TAES.2014.130708","volume":"51","author":"X Wang","year":"2015","unstructured":"Wang X, Yuan P, Mao Z (2015) Ensemble fixed-size LS-SVMs applied for the Mach number prediction in transonic wind tunnel. IEEE Trans Aerosp Electron Syst 51(4):3167\u20133181","journal-title":"IEEE Trans Aerosp Electron Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04307-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04307-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04307-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T05:11:10Z","timestamp":1721452270000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04307-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,24]]},"references-count":40,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["4307"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04307-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2019,6,24]]},"assertion":[{"value":"19 November 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}