Machine-Learning with Cellular Automata
As the possibility of combining different classifiers into Multiple Classifier System (MCS) becomes an important direction in machine-learning, difficulties arise in choosing the appropriate classifiers to combine and choosing the way for combining their decisions. Therefore in this paper we present a novel approach – Classificational Cellular Automata (CCA). The basic idea of CCA is to combine different classifiers induced on the basis of various machine-learning methods into MCS in a non-predefined way. After several iterations of applying adequate transaction rules only a set of the most appropriate classifiers for solving a specific problem is preserved.
We empirically showed that the superior results compared to AdaBoost ID3 are a direct consequence of self-organization abilities of CCA. The presented results also pointed out important advantages of CCA, such as: problem independency, robustness to noise and no need for user input.
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- 2.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)Google Scholar
- 3.Von Neumann, J.: Theory of Self-Reproducing Automata. In: Burks, A.W. (ed.), Univ. of Illi-nois Press, Urbana and London (1966)Google Scholar
- 4.Wolfram, S.: A new kind of science. Wolfram Media (2002)Google Scholar
- 5.Ganguly, N., Sikdar, B.K., Deutsch, A., Canright, G., Chaudhuri, P.P.: A survey on cellu-lar automata. Technical Report, Centre for High Performance Computing, Dresden Univer-sity of Technology (2003), http://www.cs.unibo.it/bison/pub.shtml
- 7.Towel, G., Shavlik, J.: The Extraction of Refined Rules From Knowledge Based Neural Networks. Machine Learning 131, 71–101 (1993)Google Scholar
- 8.Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases, Irvine, CA: Uni-versity of California, Department of Information and Computer Sci-ence, http://www.ics.uci.edu/~mlearn/MLRepository.html
- 9.Lenič, M., Povalej, P., Kokol, P.: Impact of purity measures on knowledge extraction in decision trees. In: Lin Tsau, Y., Setsuo, O. (eds.) Third IEEE International conference on data mining, Foundations and new directions in data mining: Workshop notes, pp. 106–111. IEEE Computer society, Los Alamitos (2003)Google Scholar