Using XCS as a Prediction Engine in Data Compression
Mohsen Sharifi1, Amir Aavani, Shahab Tasharrofi
1Computer Engineering Department, Iran University of Science
Available Online October 2007.
- 10.2991/iske.2007.112How to use a DOI?
- Learning Classifiers Systems, Data Compression, XCS, Information Theory
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.
- © 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/).
Cite this article
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 -