Using Cellular Automata for feature construction - preliminary study

Abstract

When first faced with a learning task, it is often not clear what a good representation of the training data should look like. We are often forced to create some set of features that appear plausible, without any strong confidence that they will yield superior learning. Beside, we often do not have any prior knowledge of what learning method is the best to apply, and thus often try multiple methods in an attempt to find the one that performs best. This paper describes a new method and its preliminary study for constructing features based on cellular automata (CA). Our approach uses self-organisation ability of cellular automata by constructing features being most efficient for making predictions. We present and compare the CA approach with standard genetic algorithm (GA) which both use genetic programming (GP) for constructing the features. We show and discuss some interesting properties of using CA approach in our preliminary experimental study by constructing features on synthetically generated dataset and benchmark datasets from the UCI machine learning repository. Based on the interesting results, we conclude with directions and orientation of the future work with ideas of applicability of CA approach in the feature.