Abstract
In data stream classification the problem of recurring concepts is a special case of concept drift where the underlying concepts may reappear. Several methods have been proposed to learn in the presence of concept drift, but few consider recurring concepts and context integration. To address these issues, we presented a method that stores previously learned models along with context information of that learning period. When concepts recur, the appropriate model is reused, avoiding relearning a previously seen concept. In this work, in order to model the vagueness and uncertainty associated with context, we propose the inference of high-level fuzzy contexts from fuzzy logic rules, where the conditions result from fuzzified context inputs. We also present the changes required for our method to deal with this new representation, extending the approach to handle uncertain contexts.
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
Bartolo Gomes, J.P., Menasalvas, E., Sousa, P.: Tracking recurrent concepts using context. In: Rough Sets and Current Trends in Computing, Proceedings of the Seventh International Conference RSCTC 2010 (2010) (to appear)
Gama, J., Kosina, P.: Tracking Recurring Concepts with Meta-learners. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds.) EPIA 2009. LNCS, vol. 5816, p. 423. Springer, Heidelberg (2009)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. LNCS, pp. 286–295. Springer, Heidelberg (2004)
Harries, M.B., Sammut, C., Horn, K.: Extracting hidden context. Machine Learning 32(2), 101–126 (1998)
Konar, A. (ed.): Computational Intelligence: Principles, Techniques and Applications. Springer, New York (2005)
Ranganathan, A., Al-Muhtadi, J., Campbell, R.H.: Reasoning about uncertain contexts in pervasive computing environments. IEEE Pervasive Computing 3(2), 62–70 (2004)
Tsymbal, A.: The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin (2004)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine learning 23(1), 69–101 (1996)
Yang, Y., Wu, X., Zhu, X.: Combining proactive and reactive predictions for data streams. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, p. 715. ACM, New York (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gomes, J.B., Menasalvas, E., Sousa, P.A.C. (2010). Improving the Learning of Recurring Concepts through High-Level Fuzzy Contexts. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-16248-0_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16247-3
Online ISBN: 978-3-642-16248-0
eBook Packages: Computer ScienceComputer Science (R0)