Cellular Learning Automata Approach for Data Classification

Data classification is a process that can categorize data to achieve the relationship between attributes and extract the suitable rules for prediction process. There are different learning methods in machine learning of which each has both advantages and disadvantages. Each type provides a better and interesting position, data and special structure. These methods have differences in the manner of implementation, understandability and speed of response and each is included in a special field of the data classification. Learning process in machine learning is the most important part which causes to elevate the power of a model and can learn the trained problem more quickly and work with it. In this paper, it will present a new method for data classification by Cellular Learning Automata. This method includes three stages. In order to show the power of this model, we have tested it on several types of online dataset and study it in terms of the learning speed, accuracy and simplicity in implementation with some other models and the simulated results demonstrate that the presented method provides acceptable and better answers and that one can use the proposed method for data classification.