{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:55:03Z","timestamp":1770461703277,"version":"3.49.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T00:00:00Z","timestamp":1632528000000},"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 Comput & Applic"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s00521-021-05905-y","type":"journal-article","created":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T10:02:52Z","timestamp":1632564172000},"page":"12973-12987","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Minimum variance embedded auto-associative kernel extreme learning machine for one-class classification"],"prefix":"10.1007","volume":"33","author":[{"given":"Pratik K.","family":"Mishra","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4543-6495","authenticated-orcid":false,"given":"Chandan","family":"Gautam","sequence":"additional","affiliation":[]},{"given":"Aruna","family":"Tiwari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"5905_CR1","unstructured":"(2019) TU Delft one-class dataset repository. http:\/\/homepage.tudelft.nl\/n9d04\/occ\/, Last Accessed by 21 October 2019"},{"issue":"1","key":"5905_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0893-6080(89)90014-2","volume":"2","author":"P Baldi","year":"1989","unstructured":"Baldi P, Hornik K (1989) Neural networks and principal component analysis: Learning from examples without local minima. Neural Netw 2(1):53\u201358","journal-title":"Neural Netw"},{"key":"5905_CR3","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.eswa.2018.05.009","volume":"102","author":"C Bellinger","year":"2018","unstructured":"Bellinger C, Sharma S, Japkowicz N (2018) One-class classification - from theory to practice: A case-study in radioactive threat detection. Expert Sys Appl 102:223\u2013232","journal-title":"Expert Sys Appl"},{"key":"5905_CR4","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural networks for pattern recognition","author":"C Bishop","year":"1995","unstructured":"Bishop C et al (1995) Neural networks for pattern recognition. Oxford University Press, UK"},{"issue":"16","key":"5905_CR5","doi-asserted-by":"publisher","first-page":"7182","DOI":"10.1016\/j.eswa.2014.05.037","volume":"41","author":"GG Cabral","year":"2014","unstructured":"Cabral GG, Oliveira AL (2014) One-class classification based on searching for the problem features limits. Expert Syst Appl 41(16):7182\u20137199","journal-title":"Expert Syst Appl"},{"key":"5905_CR6","doi-asserted-by":"crossref","unstructured":"Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2:1\u201327, software available at http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm","DOI":"10.1145\/1961189.1961199"},{"issue":"12","key":"5905_CR7","doi-asserted-by":"publisher","first-page":"3490","DOI":"10.1016\/j.patcog.2013.05.022","volume":"46","author":"C D\u00e9sir","year":"2013","unstructured":"D\u00e9sir C, Bernard S, Petitjean C, Heutte L (2013) One class random forests. Pattern Recogn 46(12):3490\u20133506","journal-title":"Pattern Recogn"},{"key":"5905_CR8","doi-asserted-by":"crossref","unstructured":"Diehl C, Hampshire J (2002) Real-time object classification and novelty detection for collaborative video surveillance. In: Proceedings of the 2002 International Joint Conference on Neural Networks., IEEE, vol\u00a03, pp 2620\u20132625","DOI":"10.1109\/IJCNN.2002.1007557"},{"key":"5905_CR9","unstructured":"Dua D, Graff C (2017) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"5905_CR10","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1109\/TC.1976.1674577","volume":"11","author":"R Duin","year":"1976","unstructured":"Duin R (1976) On the choice of smoothing parameters for parzen estimators of probability density functions. IEEE Trans Comput 11:1175\u20131179","journal-title":"IEEE Trans Comput"},{"issue":"5","key":"5905_CR11","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s10115-003-0132-7","volume":"6","author":"W Fan","year":"2004","unstructured":"Fan W, Miller M, Stolfo S, Lee W, Chan P (2004) Using artificial anomalies to detect unknown and known network intrusions. Knowl Inf Syst 6(5):507\u2013527","journal-title":"Knowl Inf Syst"},{"issue":"1","key":"5905_CR12","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems. J Mach Learn Res 15(1):3133\u20133181","journal-title":"J Mach Learn Res"},{"key":"5905_CR13","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.neucom.2016.04.070","volume":"261","author":"C Gautam","year":"2017","unstructured":"Gautam C, Tiwari A, Leng Q (2017) On the construction of extreme learning machine for online and offline one-class classification-an expanded toolbox. Neurocomputing 261:126\u2013143","journal-title":"Neurocomputing"},{"key":"5905_CR14","doi-asserted-by":"crossref","unstructured":"Gautam C, Tiwari A, Suresh S, Ahuja K (2019) Adaptive online learning with regularized kernel for one-class classification. IEEE Transactions on Systems, Man, and Cybernetics Systems pp 1\u201316","DOI":"10.1109\/TSMC.2019.2907672"},{"issue":"18","key":"5905_CR15","doi-asserted-by":"publisher","first-page":"7242","DOI":"10.1016\/j.eswa.2013.06.074","volume":"40","author":"GL Grinblat","year":"2013","unstructured":"Grinblat GL, Uzal LC, Granitto PM (2013) Abrupt change detection with one-class time-adaptive support vector machines. Expert Syst Appl 40(18):7242\u20137249","journal-title":"Expert Syst Appl"},{"key":"5905_CR16","volume-title":"Introduction to the Theory of Neural Computation","author":"J Hertz","year":"1991","unstructured":"Hertz J, Palmer R, Krogh A (1991) Introduction to the Theory of Neural Computation, 1st edn. Perseus Publishing, NY","edition":"1"},{"issue":"2","key":"5905_CR17","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1287\/moor.10.2.180","volume":"10","author":"D Hochbaum","year":"1985","unstructured":"Hochbaum D, Shmoys D (1985) A best possible heuristic for the k-center problem. Math Op Res 10(2):180\u2013184","journal-title":"Math Op Res"},{"key":"5905_CR18","doi-asserted-by":"crossref","unstructured":"Huang G, Zhu Q, Siew C (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), IEEE, vol\u00a02, pp 985\u2013990","DOI":"10.1109\/IJCNN.2004.1380068"},{"issue":"1\u20133","key":"5905_CR19","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G Huang","year":"2006","unstructured":"Huang G, Zhu Q, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"2","key":"5905_CR20","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s11063-016-9541-y","volume":"45","author":"A Iosifidis","year":"2017","unstructured":"Iosifidis A, Mygdalis V, Tefas A, Pitas I (2017) One-class classification based on extreme learning and geometric class information. Neural Process Lett 45(2):577\u2013592","journal-title":"Neural Process Lett"},{"key":"5905_CR21","unstructured":"Japkowicz N, Myers C, Gluck M, et\u00a0al. (1995) A novelty detection approach to classification. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp 518\u2013523"},{"issue":"6\u20137","key":"5905_CR22","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/S0167-8655(00)00131-8","volume":"22","author":"M Jiang","year":"2001","unstructured":"Jiang M, Tseng S, Su C (2001) Two-phase clustering process for outliers detection. Pattern Recogn Lett 22(6\u20137):691\u2013700","journal-title":"Pattern Recogn Lett"},{"issue":"7\u20139","key":"5905_CR23","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1016\/j.neucom.2008.05.003","volume":"72","author":"P Juszczak","year":"2009","unstructured":"Juszczak P, Tax D, Pe E, Duin R et al (2009) Minimum spanning tree based one-class classifier. Neurocomputing 72(7\u20139):1859\u20131869","journal-title":"Neurocomputing"},{"issue":"3","key":"5905_CR24","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1017\/S026988891300043X","volume":"29","author":"S Khan","year":"2014","unstructured":"Khan S, Madden M (2014) One-class classification: taxonomy of study and review of techniques. Knowl Eng Rev 29(3):345\u2013374","journal-title":"Knowl Eng Rev"},{"issue":"3\u20134","key":"5905_CR25","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s007780050006","volume":"8","author":"E Knorr","year":"2000","unstructured":"Knorr E, Ng R, Tucakov V (2000) Distance-based outliers: algorithms and applications. VLDB J-Int J Very Large Data Bases 8(3\u20134):237\u2013253","journal-title":"VLDB J-Int J Very Large Data Bases"},{"key":"5905_CR26","doi-asserted-by":"crossref","unstructured":"Koppel M, Schler J (2004) Authorship verification as a one-class classification problem. In: Proceedings of the Twenty-first International Conference on Machine Learning, ACM, New York, NY, USA, ICML \u201904, 62","DOI":"10.1145\/1015330.1015448"},{"key":"5905_CR27","doi-asserted-by":"crossref","unstructured":"Leng Q, Qi H, Miao J, Zhu W, Su G (2015) One-class classification with extreme learning machine. Mathematical problems in engineering","DOI":"10.1155\/2015\/412957"},{"issue":"7\u20139","key":"5905_CR28","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1016\/j.neucom.2006.05.013","volume":"70","author":"L Manevitz","year":"2007","unstructured":"Manevitz L, Yousef M (2007) One-class document classification via neural networks. Neurocomputing 70(7\u20139):1466\u20131481","journal-title":"Neurocomputing"},{"issue":"12","key":"5905_CR29","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1016\/j.sigpro.2003.07.018","volume":"83","author":"M Markou","year":"2003","unstructured":"Markou M, Singh S (2003a) Novelty detection: a review-part 1: statistical approaches. Sig Process 83(12):2481\u20132497","journal-title":"Sig Process"},{"issue":"12","key":"5905_CR30","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1016\/j.sigpro.2003.07.019","volume":"83","author":"M Markou","year":"2003","unstructured":"Markou M, Singh S (2003b) Novelty detection: a review-part 2: neural network based approaches. Sig Process 83(12):2499\u20132521","journal-title":"Sig Process"},{"issue":"10","key":"5905_CR31","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/TPAMI.2006.196","volume":"28","author":"M Markou","year":"2006","unstructured":"Markou M, Singh S (2006) A neural network-based novelty detector for image sequence analysis. IEEE Trans Pattern Anal Mach Intell 28(10):1664\u20131677","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5905_CR32","doi-asserted-by":"crossref","unstructured":"Mygdalis V, Iosifidis A, Tefas A, Pitas I (2016) One class classification applied in facial image analysis. In: 2016 IEEE International Conference on Image Processing (ICIP), IEEE, 1644\u20131648","DOI":"10.1109\/ICIP.2016.7532637"},{"issue":"3","key":"5905_CR33","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","volume":"33","author":"E Parzen","year":"1962","unstructured":"Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33(3):1065\u20131076","journal-title":"Ann Math Stat"},{"key":"5905_CR34","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.sigpro.2013.12.026","volume":"99","author":"M Pimentel","year":"2014","unstructured":"Pimentel M, Clifton D, Clifton L, Tarassenko L (2014) A review of novelty detection. Sig Process 99:215\u2013249","journal-title":"Sig Process"},{"issue":"7","key":"5905_CR35","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf B, Platt J, Shawe-Taylor J, Smola A, Williamson R (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443\u20131471","journal-title":"Neural Comput"},{"issue":"2","key":"5905_CR36","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.cie.2005.01.009","volume":"48","author":"H Shin","year":"2005","unstructured":"Shin H, Eom D, Kim S (2005) One-class support vector machines-an application in machine fault detection and classification. Comput Ind Eng 48(2):395\u2013408","journal-title":"Comput Ind Eng"},{"key":"5905_CR37","doi-asserted-by":"crossref","unstructured":"Sohrab F, Raitoharju J, Gabbouj M, et\u00a0al. (2018) Subspace support vector data description. In: Proceedings of the 24th International Conference on Pattern Recognition (ICPR 2018), IEEE","DOI":"10.1109\/ICPR.2018.8545819"},{"key":"5905_CR38","unstructured":"Tax D (2002) One-class classification: Concept learning in the absence of counter-examples"},{"key":"5905_CR39","unstructured":"Tax D (2018) Ddtools, the data description toolbox for matlab. Version 2(1):3"},{"issue":"1","key":"5905_CR40","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"D Tax","year":"2004","unstructured":"Tax D, Duin R (2004) Support vector data description. Mach Learn 54(1):45\u201366","journal-title":"Mach Learn"},{"key":"5905_CR41","doi-asserted-by":"crossref","unstructured":"Ypma A, Duin R (1998) Support objects for domain approximation. In: International Conference on Artificial Neural Networks, Springer, pp 719\u2013724","DOI":"10.1007\/978-1-4471-1599-1_110"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05905-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05905-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05905-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T11:02:55Z","timestamp":1699527775000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05905-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,25]]},"references-count":41,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["5905"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05905-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,25]]},"assertion":[{"value":"27 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 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"}}]}}