CAP: A cellular automata based fuzzy classifier

Abstract

This paper proposes an efficient classifier based on continuous cellular automata with promising running time and classification accuracy. The classifier model is capable of processing data sets containing large number of numeric as well as non-numeric attributes. It performs effective pre-processing of numeric and nominal attributes to identify the most relevant ones. It also prunes out unnecessary computations by limiting stabilisation of the cellular automata during the training phase. The classifier also employs various techniques to reduce the number of incorrect classifications and overfitting of data.A number of experiments were conducted to evaluate the performance of the proposed classifier by changing various parameters during the training phase. Evaluations show that the classifier out-performs existing rule-based classifiers in terms of accuracy.