Multiple Attractor Cellular Automata Classification Method and Over-Fitting Problem with CART

The classification methods based on multiple attractor cellular automata can process the classification of two classes, and they are difficult to overcome overfitting problem. There are not yet effective methods for constructing a multiple attractor cellular automata which can process multi-classification and overfitting problem. The pattern space partition in the view of cell space is a kind of uniform partition which is difficult to adapt to the needs of spatial non-uniform partition. By combining the CART algorithm with the multiple attractor cellular automata, a kind of classifier with tree structure is constructed to solve the non-uniform partition problem and overfitting problem. The multiple attractor cellular automata characteristic matrix is defined, and the learning method of classifiers as a node in a tree is studied based on particle swarm optimization algorithm. The multiple attractor cellular automata classifiers built on this approach are able to obtain good classification performance by using less number of bits of pseudo-exhaustive field. The classifier with tree frame of multiple attractor cellular automata reduces the number of empty basin and restrains overfitting problem without lost accurate rate, and shorts the classification time. The feasibility and the effectiveness of the proposed method have been verified by experiments.