Using cellular automata for feature construction - Preliminary study

M. Mertik, M. Pechenizkiy, G. Stiglic, P. Kokol

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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.
Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Digital Ecosystems and Technologies Conference (DEST 2007) 21-23 February 2007, Cairns, Australia
Place of PublicationPiscataway, New Jersey, USA
PublisherIEEE Computer Society
ISBN (Print)1-4244-0470-3
Publication statusPublished - 2007
Eventconference; DEST 2007, Cairns, Australia; 2007-02-21; 2007-02-23 -
Duration: 21 Feb 200723 Feb 2007


Conferenceconference; DEST 2007, Cairns, Australia; 2007-02-21; 2007-02-23
OtherDEST 2007, Cairns, Australia

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