A New Compaction Algorithm for LCS Rules - Breast Cancer Dataset Case Study

Faten Kharbat, Larry Bull, Mohammed Odeh

Abstract

This paper introduces a new compaction algorithm for the rules generated by learning classifier systems that overcomes the disadvantages of previous algorithms in complexity, compacted solution size, accuracy and usability. The algorithm is tested on a Wisconsin Breast Cancer Dataset (WBC) which is a well well-known breast cancer datasets from the UCI Machine Learning Repository.

References

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Paper Citation


in Harvard Style

Kharbat F., Bull L. and Odeh M. (2012). A New Compaction Algorithm for LCS Rules - Breast Cancer Dataset Case Study . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 382-385. DOI: 10.5220/0004167403820385


in Bibtex Style

@conference{kdir12,
author={Faten Kharbat and Larry Bull and Mohammed Odeh},
title={A New Compaction Algorithm for LCS Rules - Breast Cancer Dataset Case Study},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={382-385},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004167403820385},
isbn={978-989-8565-29-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - A New Compaction Algorithm for LCS Rules - Breast Cancer Dataset Case Study
SN - 978-989-8565-29-7
AU - Kharbat F.
AU - Bull L.
AU - Odeh M.
PY - 2012
SP - 382
EP - 385
DO - 10.5220/0004167403820385