Abstract
Mining data streams is a field of study that poses new challenges. This research delves into the study of applying different techniques of classification of data streams, and carries out a comparative analysis with a proposal based on similarity; introducing a new form of management of representative data models and policies of insertion and removal, advancing also in the design of appropriate estimators to improve classification performance and updating of the model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Orriols-puig, A., Casillas, J., Bernado, E.: Fuzzy-UCS: A Michigan-style Learning Fuzzy-Classifier System for Supervised Learning. Transactions on Evolutionary Computation, 1–23 (2008)
Polikar, R., Udpa, L., Udpa, S.S., Honavar, V.: LEARN ++: an Incremental Learning Algorithm For Multilayer Perceptron Networks. IEEE Transactions on System, Man and Cybernetics (C), Special Issue on Knowledge Management, 3414–3417 (2000)
Ferrer, F.J., Aguilar, J.S., Riquelme, J.C.: Incremental Rule Learning and Border Examples Selection from Numerical Data Streams. Journal of Universal Computer Science, 1426–1439 (2005)
Widmer, G.: Combining Robustness and Flexibility in Learning Drifting Concepts. Machine Learning, 1–11 (1994)
Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Machine Learning 1(3), 317–354 (1986)
Watanabe, L., Elio, R.: Guiding Constructive Induction for Incremental Learning from Examples. Knowledge Acquisition, 293–296 (1987)
Kolter, J.Z., Maloof, M.A.: Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift. In: Proceedings of the Third International IEEE Conference on Data Mining, pp. 123–130 (2003)
Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)
Jin, R., Agrawal, G.: Efficient Decision Tree Construction on Streaming Data. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 571 – 576(2003)
Ramos-jim, G., Jos, R.M.-b., Avila, C.: IADEM-0: Un Nuevo Algoritmo Incremental, pp. 91–98 (2004)
Beringer, J., Hullermeier, E.: Efficient Instance-Based Learning on Data Streams. Intelligent Data Analysis, 1–43 (2007)
Salganicoff, M.: Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching. Artificial Intelligence Review, 133–155 (1997)
Klinkenberg, R., Joachims, T.: Detecting Concept Drift with Support Vector Machines. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pp. 487–494 (2000)
Mukherjee, K.: Application of the Gabriel Graph to Instance Based Learning Algorithms. PhD thesis, Simon Fraser University (2004)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Machine Learning 66, 37–66 (1991)
Randall Wilson, D., Martinez, T.R.: Improved Heterogeneous Distance Functions. Artificial Intelligence 6, 1–34 (1997)
Stanfill, C., Waltz, D.: Toward memory-based reasoning. Communications of the ACM 29(12), 1213–1228 (1986)
Gama, J., Medas, P., Rocha, R.: Forest Trees for On-line Data. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 632–636 (2004)
Gama, J., Rocha, R., Medas, P.: Accurate Decision Trees for Mining High-speed Data Streams. In: Proc. SIGKDD, pp. 523–528 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mena Torres, D., Aguilar Ruiz, J., Rodríguez Sarabia, Y. (2011). Classification Model for Data Streams Based on Similarity. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-21822-4_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21821-7
Online ISBN: 978-3-642-21822-4
eBook Packages: Computer ScienceComputer Science (R0)