Using XCS as a Prediction Engine in Data Compression

DOI

10.2991/iske.2007.112How to use a DOI?

Keywords

Learning Classifiers Systems, Data Compression, XCS, Information Theory

Abstract

XCS has been used in several fields as a prediction engine before; its population based nature enables XCS to generate sets of properly pruned classification rules. Further, unlike other population based algorithms, it learns using rewards. These properties encouraged us to use it as a predictor engine for lossless data compression. In the compression context, XCS can be used to find the hidden relations in files of the same type. So, we used it as a preprocessor before an entropy encoder, to remove the existing correlations between file’s bits or symbols. Removing correlations causes entropy encoders to achieve higher rates of compression. The results support this conclusion.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Mohsen Sharifi
AU  - Amir Aavani
AU  - Shahab Tasharrofi
PY  - 2007/10
DA  - 2007/10
TI  - Using XCS as a Prediction Engine in Data Compression
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 655
EP  - 661
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.112
DO  - 10.2991/iske.2007.112
ID  - Sharifi2007/10
ER  -