A cellular automata-based learning method for classification

• We highlighted the main impact and significant in abstract.
• We augmented a separate section regarding theoretical analysis.
• We rewrote the sections of introduction and conclusions.

Over the last few decades, classification applied to numerous applications in science, engineering, business and industries have rapidly been increased, especially for big data. However, classifiers dealing with complicated high dimension problems with non-conforming patterns with high accuracy are rare, especially for bit-level features. It is a challenging research problem. This paper proposed a novel efficient classifier based on cellular automata model, called Cellular Automata-based Classifier (CAC). CAC possesses the promising capability to deal with non-conforming patterns in the bit-level features. It was developed on a new kind of the proposed elementary cellular automata, called Decision Support Elementary Cellular Automata (DS-ECA). The classification capability of DS-ECA is promising since it can describe very complicated decision rule in high dimension problems with less complexity. CAC comprises double rule vectors and a decision function, the structure of which has two layers; the first layer is employed to evolve an input pattern into feature space and the other interprets the patterns in feature space as binary answer through the decision function. It has a time complexity of learning at O(n2), while the classification for one instance is O(1), where n is a number of bit patterns. For classification performance, 12 datasets consisting of binary and non-binary features are empirically implemented in comparison with Support Vector Machines (SVM) using k-fold cross validation. In this respect, CAC outperforms SVM with the best kernel for binary features, and provides the promising results equivalent to SVM on average for non-binary features.