{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:17:34Z","timestamp":1761895054206,"version":"3.37.3"},"publisher-location":"Berlin, Heidelberg","reference-count":25,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642121470"},{"type":"electronic","value":"9783642121487"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010]]},"DOI":"10.1007\/978-3-642-12148-7_7","type":"book-chapter","created":{"date-parts":[[2010,3,31]],"date-time":"2010-03-31T15:19:10Z","timestamp":1270048750000},"page":"74-85","source":"Crossref","is-referenced-by-count":7,"title":["Handling Different Categories of Concept Drifts in Data Streams Using Distributed GP"],"prefix":"10.1007","author":[{"given":"Gianluigi","family":"Folino","sequence":"first","affiliation":[]},{"given":"Giuseppe","family":"Papuzzo","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"7_CR1","unstructured":"Barbar\u00e1, D.: Chaotic mining: Knowledge discovery using the fractal dimension. In: 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (1999)"},{"issue":"2","key":"7_CR2","first-page":"123","volume":"24","author":"L. Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Machine Learning\u00a024(2), 123\u2013140 (1996)","journal-title":"Machine Learning"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G.: An experimental comparison of three methods for costructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning\u00a0(40), 139\u2013157 (2000)","DOI":"10.1023\/A:1007607513941"},{"issue":"5","key":"7_CR4","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1109\/TEVC.2005.863627","volume":"10","author":"G. Folino","year":"2006","unstructured":"Folino, G., Pizzuti, C., Spezzano, G.: Ensembles for large scale data classification. IEEE Transaction on Evolutionary Computation\u00a010(5), 604\u2013616 (2006)","journal-title":"IEEE Transaction on Evolutionary Computation"},{"key":"7_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/978-3-540-71605-1_15","volume-title":"Genetic Programming","author":"G. Folino","year":"2007","unstructured":"Folino, G., Pizzuti, C., Spezzano, G.: Mining distributed evolving data streams using fractal gp ensembles. In: Ebner, M., O\u2019Neill, M., Ek\u00e1rt, A., Vanneschi, L., Esparcia-Alc\u00e1zar, A.I. (eds.) EuroGP 2007. LNCS, vol.\u00a04445, pp. 160\u2013169. Springer, Heidelberg (2007)"},{"issue":"4","key":"7_CR6","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1109\/TEVC.2007.906658","volume":"12","author":"G. Folino","year":"2008","unstructured":"Folino, G., Pizzuti, C., Spezzano, G.: Training distributed gp ensemble with a selective algorithm based on clustering and pruning for pattern classification. IEEE Trans. Evolutionary Computation\u00a012(4), 458\u2013468 (2008)","journal-title":"IEEE Trans. Evolutionary Computation"},{"key":"7_CR7","unstructured":"Freund, Y., Scapire, R.: Experiments with a new boosting algorithm. In: Proceedings of the 13th Int. Conference on Machine Learning, pp. 148\u2013156 (1996)"},{"key":"7_CR8","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/0375-9601(83)90753-3","volume":"97A","author":"P. Grassberger","year":"1983","unstructured":"Grassberger, P.: Generalized dimensions of strange attractors. Physics Letters\u00a097A, 227\u2013230 (1983)","journal-title":"Physics Letters"},{"key":"7_CR9","first-page":"1053","volume-title":"Proc. of the Genetic and Evolutionary Computation Conference GECCO 1999","author":"H. Iba","year":"1999","unstructured":"Iba, H.: Bagging, boosting, and bloating in genetic programming. In: Proc. of the Genetic and Evolutionary Computation Conference GECCO 1999, Orlando, Florida, July 1999, pp. 1053\u20131060. Morgan Kaufmann, San Francisco (1999)"},{"key":"7_CR10","first-page":"2755","volume":"8","author":"J.Z. Kolter","year":"2007","unstructured":"Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: An ensemble method for drifting concepts. J. Mach. Learn. Res.\u00a08, 2755\u20132790 (2007)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR11","first-page":"66","volume-title":"Proc. of the Genetic and Evolutionary Computation Conference GECCO 2001","author":"W.B. Langdon","year":"2001","unstructured":"Langdon, W.B., Buxton, B.F.: Genetic programming for combining classifiers. In: Proc. of the Genetic and Evolutionary Computation Conference GECCO 2001, July 2001, pp. 66\u201373. Morgan Kaufmann, San Francisco (2001)"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Liebovitch, L., Toth, T.: A fast algorithm to determine fractal dimensions by box counting. Physics Letters\u00a0141A(8) (1989)","DOI":"10.1016\/0375-9601(89)90854-2"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Lin, G., Chen, L.: A grid and fractal dimension-based data stream clustering algorithm. In: International Symposium on Information Science and Engieering, vol.\u00a01, pp. 66\u201370 (2008)","DOI":"10.1109\/ISISE.2008.141"},{"key":"7_CR14","volume-title":"The Fractal Geometry of Nature","author":"B. Mandelbrot","year":"1983","unstructured":"Mandelbrot, B.: The Fractal Geometry of Nature. W.H Freeman, New York (1983)"},{"key":"7_CR15","unstructured":"Minku, L.L., White, A.P., Yao, X.: The impact of diversity on on-line ensemble learning in the presence of concept drift. IEEE Transactions on Knowledge and Data Engineering\u00a099(1), 5555"},{"key":"7_CR16","first-page":"725","volume-title":"Proceedings of the 13th National Conference on Artificial Intelligence AAAI 1996","author":"J. Ross Quinlan","year":"1996","unstructured":"Ross Quinlan, J.: Bagging, boosting, and c4.5. In: Proceedings of the 13th National Conference on Artificial Intelligence AAAI 1996, pp. 725\u2013730. MIT Press, Cambridge (1996)"},{"key":"7_CR17","unstructured":"Sarraille, J., DiFalco, P.: FD3, http:\/\/tori.postech.ac.kr\/softwares"},{"issue":"2","key":"7_CR18","first-page":"197","volume":"5","author":"R.E. Schapire","year":"1990","unstructured":"Schapire, R.E.: The strength of weak learnability. Machine Learning\u00a05(2), 197\u2013227 (1990)","journal-title":"Machine Learning"},{"issue":"2","key":"7_CR19","first-page":"256","volume":"121","author":"R.E. Schapire","year":"1996","unstructured":"Schapire, R.E.: Boosting a weak learning by majority. Information and Computation\u00a0121(2), 256\u2013285 (1996)","journal-title":"Information and Computation"},{"issue":"3","key":"7_CR20","first-page":"317","volume":"1","author":"J.C. Schlimmer","year":"1986","unstructured":"Schlimmer, J.C., Granger Jr., R.H.: Incremental learning from noisy data. Mach. Learn.\u00a01(3), 317\u2013354 (1986)","journal-title":"Mach. Learn."},{"key":"7_CR21","unstructured":"Sousa, E.P.M., Ribeiro, M.X., Traina, A.J.M., Traina Jr., C.: Tracking the intrinsic dimension of evolving data streams to update association rules. In: 3rd International Workshop on Knowledge Discovery from Data Streams, part of the 23th International Conference on Machine Learning, ICML 2006 (2006)"},{"key":"7_CR22","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1145\/502512.502568","volume-title":"Proceedings of the seventh ACM SIGKDD International conference on Knowledge discovery and data mining (KDD 2001)","author":"W.N. Street","year":"2001","unstructured":"Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the seventh ACM SIGKDD International conference on Knowledge discovery and data mining (KDD 2001), San Francisco, CA, USA, August 26-29, pp. 377\u2013382. ACM, New York (2001)"},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.physd.2007.07.004","volume":"237","author":"M. Tykierko","year":"2008","unstructured":"Tykierko, M.: Using invariants to change detection in dynamical system with chaos. Physica D Nonlinear Phenomena\u00a0237, 6\u201313 (2008)","journal-title":"Physica D Nonlinear Phenomena"},{"key":"7_CR24","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1023\/A:1022699900025","volume":"4","author":"P.E. Utgoff","year":"1989","unstructured":"Utgoff, P.E.: Incremental induction of decision trees. Machine Learning\u00a04, 161\u2013186 (1989)","journal-title":"Machine Learning"},{"key":"7_CR25","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1145\/956750.956778","volume-title":"Proceedings of the nineth ACM SIGKDD International conference on Knowledge discovery and data mining (KDD 2003)","author":"H. Wang","year":"2003","unstructured":"Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the nineth ACM SIGKDD International conference on Knowledge discovery and data mining (KDD 2003), Washington, DC, USA, August 24-27, pp. 226\u2013235. ACM, New York (2003)"}],"container-title":["Lecture Notes in Computer Science","Genetic Programming"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-12148-7_7.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T20:07:33Z","timestamp":1739995653000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-12148-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010]]},"ISBN":["9783642121470","9783642121487"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-12148-7_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2010]]}}}