Probabilistic 2D cellular automata rules for binary classification

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. Fawcetts rule is modified into two proposed forms partially and fully probabilistic. The effectiveness of classifications of these three methods is analysed and compared. The classification methods are used as the rules in the two-dimensional 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.