An Efficient Cellular Automata-Based Classifier with Variance Decision Table

Classification is an important task of machine learning for solving a wide range of problems in conforming patterns. In the literature, machine learning algorithms dealing with non-conforming patterns are rarely proposed. In this regard, a cellular automata-based classifier (CAC) was proposed to deal with non-conforming binary patterns. Unfortunately, its ability to cope with high-dimensional and complicated problems is limited due to its applying a traditional genetic algorithm in rule ordering in CAC. Moreover, it has no mechanism to cope with ambiguous and inconsistent decision tables. Therefore, a novel proposed algorithm, called a cellular automata-based classifier with a variance decision table (CAV), was proposed to address these limitations. Firstly, we apply a novel butterfly optimization, enhanced with a mutualism scheme (m-MBOA), to manage the rule ordering in high dimensional and complicated problems. Secondly, we provide the percent coefficient of variance in creating a variance decision table, and generate a variance coefficient to estimate the best rule matrices. Thirdly, we apply a periodic boundary condition in a cellular automata (CA) boundary scheme in lieu of a null boundary condition to improve the performance of the initialized process. Empirical experiments were carried out on well-known public datasets from the OpenML repository. The experimental results show that the proposed CAV model significantly outperformed the compared CAC model and popular classification methods.