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

Faten Kharbat; Larry Bull; Mohammed Odeh

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Paper

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

Topics: Information Extraction; Machine Learning; Mining High-Dimensional Data

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 0IC3K, 382-385, 2012 , Barcelona, Spain

Authors: Faten Kharbat ¹ ; Larry Bull ² and Mohammed Odeh ²

Affiliations: ¹ Emirates College of Technology, United Arab Emirates ; ² University of the West of England, United Kingdom

Keyword(s): Learning Classifier System, Compaction Algorithm, XCS.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining High-Dimensional Data ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:

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 (IC3K 2012) - KDIR; ISBN 978-989-8565-29-7; ISSN 2184-3228, SciTePress, pages 382-385. DOI: 10.5220/0004167403820385

@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 (IC3K 2012) - KDIR},
year={2012},
pages={382-385},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004167403820385},
isbn={978-989-8565-29-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2012) - KDIR
TI - A New Compaction Algorithm for LCS Rules - Breast Cancer Dataset Case Study
SN - 978-989-8565-29-7
IS - 2184-3228
AU - Kharbat, F.
AU - Bull, L.
AU - Odeh, M.
PY - 2012
SP - 382
EP - 385
DO - 10.5220/0004167403820385
PB - SciTePress