A Cellular Automata based Classification Algorithm

Abstract

Data classification is a well studied problem where the aim is to identify the categories in the data based on a training set. Various machine learning methods have been utilized for the problem. On the other side, cellular automata have drawn the attention of researchers as the system provides a dynamic and a discrete model for computation. In this study a novel approach is proposed for the classification problem. The method is based on formation of classes in a cellular automata by the interaction of neighborhood cells. Initially, the training data instances are assigned to the cells of a cellular automaton. The state of a cell denotes the class assignment of that point in the instance space. At the beginning of the process, only the cells that have a data instance have class assignments. However, these class assignments are spread to the neighbor cells based on a rule inspired by the heat transfer process in nature. The experiments carried out denote that the model can identify the categories in the data and promising results have been obtained.