{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T14:08:52Z","timestamp":1744898932715},"reference-count":11,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2013,12]]},"abstract":"<jats:p> This work describes the use of a weighted ensemble of neural network classifiers for adaptive learning. We train the neural networks by means of a quantum-inspired evolutionary algorithm (QIEA). The QIEA is also used to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. After running several simulations using two different datasets and performing two different analysis of the results, we show that the proposed algorithm, named neuro-evolutionary ensemble (NEVE), was able to learn the data set and to quickly respond to any drifts on the underlying data, indicating that our model can be a good alternative to address concept drift problems. We also compare the results obtained by our model with an existing algorithm, Learn++.NSE, in two different nonstationary scenarios. <\/jats:p>","DOI":"10.1142\/s1469026813400026","type":"journal-article","created":{"date-parts":[[2013,12,30]],"date-time":"2013-12-30T07:58:21Z","timestamp":1388390301000},"page":"1340002","source":"Crossref","is-referenced-by-count":7,"title":["LEARNING UNDER CONCEPT DRIFT USING A NEURO-EVOLUTIONARY ENSEMBLE"],"prefix":"10.1142","volume":"12","author":[{"given":"TATIANA","family":"ESCOVEDO","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, (PUC-Rio) Rio de Janeiro, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ANDR\u00c9 V. ABS","family":"DA CRUZ","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, (PUC-Rio) Rio de Janeiro, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MARLEY M. B. R.","family":"VELLASCO","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, (PUC-Rio) Rio de Janeiro, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ADRIANO S.","family":"KOSHIYAMA","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, (PUC-Rio) Rio de Janeiro, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2014,1,3]]},"reference":[{"key":"rf1","first-page":"317","volume":"1","author":"Schlimmer J. C.","journal-title":"Mach. Learn."},{"key":"rf2","author":"Tsymbal A.","journal-title":"Tech. Rep."},{"key":"rf5","first-page":"1517","volume":"10","author":"Elwell R.","journal-title":"IEEE Trans. Neural Netw."},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1002\/0471660264.ch3"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2002.804320"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2004.823467"},{"key":"rf16","volume-title":"Design and Analysis of Experiments","author":"Montgomery D. C.","year":"2008"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.2307\/2347973"},{"key":"rf19","volume-title":"Introductory Statistics with R","author":"Dalgaard P.","year":"2002"},{"key":"rf20","first-page":"2755","volume":"8","author":"Kolter J.","journal-title":"J. Mach. Learn. Res."},{"key":"rf21","first-page":"1","volume":"8","author":"Jackowski K.","journal-title":"Pattern Anal. Appl."}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026813400026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T05:18:23Z","timestamp":1565155103000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1469026813400026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,12]]},"references-count":11,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2014,1,3]]},"published-print":{"date-parts":[[2013,12]]}},"alternative-id":["10.1142\/S1469026813400026"],"URL":"https:\/\/doi.org\/10.1142\/s1469026813400026","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,12]]}}}