Abstract
In real world there are many examples where synergetic cooperation of multiple entities performs better than just single one. The same fundamental idea can be found in ensemble learning methods that have the ability to improve classification accuracy. Each classifier has specific view on the problem domain and can produce different classification for the same observed sample. Therefore many methods for combining classifiers into ensembles have been already developed. Most of them use simple majority voting or weighted voting of classifiers to combine the single classifier votes. In this paper we present a new approach for combining classifiers into an ensemble with Classificational Cellular Automata (CCA), which exploit the cellular automata self-organizational abilities. We empirically show that CCA improves the classification accuracy of three popular ensemble methods: Bagging, Boosting and MultiBoosting. The presented results also show important advantages of CCA, such as: problem independency, robustness to noise and no need for the user input.
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
Wolpert, D., Macready, W.: No Free Lunch Theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)
Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996), http://citeseer.ist.psu.edu/breiman96bagging.html
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings Thirteenth International Conference on Machine Learning, pp. 148–156. Morgan Kaufman, San Francisco (1996)
Dietterich, T.,G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) First International Workshop on Multiple Classifier Systems. LNCS, pp. 1–15. Springer, New York (2000)
Kokol, P., Povalej, P., Lenič, M., Štiglic, G.: Building classifier cellular automata. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) ACRI 2004. LNCS, vol. 3305, pp. 823–830. Springer, Heidelberg (2004)
Neumann, J.: Theory of Self-Reproducing Automata. In: Burks, A.W. (ed.). Univ. of Illinois Press, Urbana (1966)
Witten, H.I., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA, http://www.ics.uci.edu/~mlearn/MLRepository.html
Webb, G.: MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning 40(2), 159–196 (2000)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Povalej, P., Lenič, M., Kokol, P. (2005). Improving Ensembles with Classificational Cellular Automata. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_32
Download citation
DOI: https://doi.org/10.1007/11508069_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
eBook Packages: Computer ScienceComputer Science (R0)