Combining online classification approaches for changing environments

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5342 LNCS, Issue , 2008, Pages 520-529

  Rodríguez, Juan J ^a   Kuncheva, Ludmila I ^b  

^a UNIVERSITY OF BURGOS   (Spain)

^b BANGOR UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; PATTERN RECOGNITION; SURFACE PLASMON RESONANCE; SYNTACTICS; TECHNICAL PRESENTATIONS;

ABRUPT CHANGES; CHANGE DETECTIONS; CHANGING ENVIRONMENTS; CLASSIFICATION APPROACHES; CLASSIFIER ENSEMBLES; DATA DISTRIBUTIONS; DATA SETS; GRADUAL CHANGES; LEARNING RATES; LINEAR DISCRIMINANT CLASSIFIERS; NEAREST NEIGHBOURS; ONLINE VERSIONS; PERCEPTRON;

CLASSIFIERS;

EID: 58349087238     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89689-0_56     Document Type: Conference Paper

References (10)

1. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1-30 (2006)
2. Fürnkranz, J.: Round robin classification. Journal of Machine Learning Research 2, 721-747 (2002)
3. Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS, vol. 3171, pp. 286-295. Springer, Heidelberg (2004)
4. Kuncheva, L.I.: Classifier ensembles for changing environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1-15. Springer, Heidelberg (2004)
5. Littlestone, N.: Learning quickly when irrelevant attributes abound: A new linear threshold algorithm. Machine Learning 2(4), 285-318 (1988)
6. Núñez, M., Fidalgo, R., Morales, R.: Learning in environments with unknown dynamics: Towards more robust concept learners. Journal of Machine Learning Research 8, 2595-2628 (2007)
7. Polikar, R., Udpa, L., Udpa, S.S., Honavar, V.: Learn++: An incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man and Cybernetics 31(4), 497-508 (2001)
8. Reynolds, M.R., Stoumbos, Z.G.: The SPRT chart for monitoring a proportion. HE Transactions 30(6), 545-561 (1998)
9. Stanley, K.O.: Learning concept drift with a committee of decision trees. Technical Report AI-03-302, Computer Science Department, University of Texas-Austin (2003)
10. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: KDD 2003: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 226-235. ACM, New York (2003)