Abstract
This paper proposes a cellular automata-based solution of a binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann neighborhood. Since the number of possible CA rules (potential CA-based classifiers) is huge, searching efficient rules is conducted with use of a genetic algorithm (GA). Experiments show an excellent performance of discovered rules in solving the classification problem. The best found rules perform better than the heuristic CA rule designed by a human and also better than one of the most widely used statistical method: the k-nearest neighbors algorithm (k-NN). Experiments show that CAs rules can be successfully reused in the process of searching new rules.
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This research was supported by the grant S/WI/2/2008 from Bialystok University of Technology.
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Piwonska, A., Seredynski, F. & Szaban, M. Learning cellular automata rules for binary classification problem. J Supercomput 63, 800–815 (2013). https://doi.org/10.1007/s11227-012-0767-9
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DOI: https://doi.org/10.1007/s11227-012-0767-9