An improved multiple-attractor cellular automata classifier with a tree frame based on CART

From the view of a cell, the partition of a pattern space is a uniform partition. It is difficult to meet the needs of spatial non-uniform partitioning. In this paper, a cellular automaton classifier with a tree structure is proposed, by combining multiple-attractor cellular automata with the algorithm CART. The method of construction of the characteristic matrix of the multiple-attractor cellular automata is studied on the basis of particle swarm optimization. This method builds multiple-attractor cellular automata as tree nodes. This kind of classifier can be used to solve the non-uniform partition problem and obtain a good classification performance by using a pseudo-exhaustive field with a few bits, and so can restrain the over-fitting. The feasibility and the effectiveness of this method have been verified by experiments.