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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Probabilistic 2D Cellular Automata Rules for Binary Classification

DOI: http://dx.doi.org/10.15439/2016F409

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 161–164 ()

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Abstract. In this paper are presented classification methods with use of two-dimensional three-state cellular automata. This methods are probabilistic forms of cellular automata rule modified from wide known almost deterministic rule designed by Fawcett, known as n4\_V 1\_nonstable. Fawcetts rule is modified into two proposed forms partially (n4\_V 1\_nonstable\_PP) and fully probabilistic (n4\_V 1\_nonstable\_FP). The effectiveness of classifications of these three methods is analysed and compared. The classification methods are used as the rules in the twodimensional three-state cellular automaton with the von Neumann and Moore neighbourhood. Preliminary experiments show that probabilistic modification of Fawcett's method can give better results in the process of reconstruction (classification) than the original algorithm.

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