{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T22:09:40Z","timestamp":1725746980182},"publisher-location":"Berlin, Heidelberg","reference-count":18,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642411410"},{"type":"electronic","value":"9783642411427"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013]]},"DOI":"10.1007\/978-3-642-41142-7_64","type":"book-chapter","created":{"date-parts":[[2013,9,3]],"date-time":"2013-09-03T13:37:15Z","timestamp":1378215435000},"page":"636-645","source":"Crossref","is-referenced-by-count":4,"title":["NEVE: A Neuro-Evolutionary Ensemble for Adaptive Learning"],"prefix":"10.1007","author":[{"given":"Tatiana","family":"Escovedo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9 Vargas Abs","family":"da Cruz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marley","family":"Vellasco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adriano Soares","family":"Koshiyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"issue":"3","key":"64_CR1","first-page":"317","volume":"1","author":"J.C. Schlimmer","year":"1986","unstructured":"Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Machine Learning\u00a01(3), 317\u2013354 (1986)","journal-title":"Machine Learning"},{"issue":"10","key":"64_CR2","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/TNN.2011.2160459","volume":"22","author":"R. Elwell","year":"2011","unstructured":"Elwell, R., Polikar, R.: Incremental Learning of Concept drift in Nonstationary Environments. IEEE Transactions on Neural Networks\u00a022(10), 1517\u20131531 (2011)","journal-title":"IEEE Transactions on Neural Networks"},{"key":"64_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-540-25966-4_1","volume-title":"Multiple Classifier Systems","author":"L.I. Kuncheva","year":"2004","unstructured":"Kuncheva, L.I.: Classifier ensembles for changing environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol.\u00a03077, pp. 1\u201315. Springer, Heidelberg (2004)"},{"key":"64_CR4","unstructured":"Kuncheva, L.I.: Classifier ensemble for detecting concept change in streaming data: Overview and perspectives. In: Proc. Eur. Conf. Artif. Intell., pp. 5\u201310 (2008)"},{"key":"64_CR5","unstructured":"Ahiskali, M.T.M., Muhlbaier, M., Polikar, R.: Learning concept drift in non-stationary environments using an ensemble of classifiers based approach. IJCNN, 3455\u20133462 (2008)"},{"key":"64_CR6","volume-title":"Online Ensemble Learning","author":"N.C. Oza","year":"2001","unstructured":"Oza, N.C.: Online Ensemble Learning. Dissertation, University of California, Berkeley (2001)"},{"key":"64_CR7","unstructured":"Abs da Cruz, A.V., Vellasco, M.M.B.R., Pacheco, M.A.C.: Quantum-inspired evo-lutionary algorithms for numerical optimization problems. In: Proceedings of the IEEE World Conference in Computational Intelligence (2006)"},{"key":"64_CR8","unstructured":"Abs da Cruz, A.V.: Algoritmos evolutivos com inspira\u00e7\u00e3o qu\u00e2ntica para otimiza\u00e7\u00e3o de problemas com representa\u00e7\u00e3o num\u00e9rica. Ph.D. dissertation, Pontifical Catholic University \u2013 Rio de Janeiro (2007)"},{"issue":"6","key":"64_CR9","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1109\/TEVC.2002.804320","volume":"6","author":"K.-H. Han","year":"2002","unstructured":"Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evolutionary Computation\u00a06(6), 580\u2013593 (2002)","journal-title":"IEEE Trans. Evolutionary Computation"},{"key":"64_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/3-540-45105-6_52","volume-title":"Genetic and Evolutionary Computation - GECCO 2003","author":"K.-H. Han","year":"2003","unstructured":"Han, K.-H., Kim, J.-H.: On setting the parameters of QEA for practical applications: Some guidelines based on empirical evidence. In: Cant\u00fa-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol.\u00a02723, pp. 427\u2013428. Springer, Heidelberg (2003)"},{"issue":"2","key":"64_CR11","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/TEVC.2004.823467","volume":"8","author":"K.-H. Han","year":"2004","unstructured":"Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithms with a new termination criterion, He gate, and two-phase scheme. IEEE Trans. Evolutionary Computation\u00a08(2), 156\u2013169 (2004)","journal-title":"IEEE Trans. Evolutionary Computation"},{"key":"64_CR12","doi-asserted-by":"crossref","unstructured":"Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proc. 7th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., pp. 377\u2013382 (2001)","DOI":"10.1145\/502512.502568"},{"key":"64_CR13","unstructured":"Polikar, R., Elwell, R.: Benchmark Datasets for Evaluating Concept drift\/NSE Algorithms, \n                    \n                      http:\/\/users.rowan.edu\/~polikar\/research\/NSE\n                    \n                    \n                   (last access at December 2012)"},{"key":"64_CR14","unstructured":"Montgomery, D.C.: Design and analysis of experiments. Wiley (2008)"},{"key":"64_CR15","unstructured":"R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria (2012), Donwload at: \n                    \n                      www.r-project.org"},{"key":"64_CR16","doi-asserted-by":"publisher","first-page":"115","DOI":"10.2307\/2347973","volume":"31","author":"P. Royston","year":"1982","unstructured":"Royston, P.: An extension of Shapiro and Wilk\u2019s W test for normality to large samples. Applied Statistics\u00a031, 115\u2013124 (1982)","journal-title":"Applied Statistics"},{"key":"64_CR17","first-page":"2755","volume":"8","author":"J. Kolter","year":"2007","unstructured":"Kolter, J., Maloof, M.: Dynamic weighted majority: An ensemble method for drifting concepts. Journal of Machine Learning Research\u00a08, 2755\u20132790 (2007)","journal-title":"Journal of Machine Learning Research"},{"key":"64_CR18","doi-asserted-by":"crossref","unstructured":"Jackowski, K.: Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers. Pattern Analysis and Applications (2013)","DOI":"10.1007\/s10044-013-0318-x"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-41142-7_64","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,17]],"date-time":"2019-05-17T01:19:29Z","timestamp":1558055969000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-41142-7_64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013]]},"ISBN":["9783642411410","9783642411427"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-41142-7_64","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2013]]}}}