SWARM INTELLIGENCE FOR RULE DISCOVERY IN DATA MINING

Andre B. de Carvalho, Taylor Savegnago, Aurora Pozo

Abstract

This paper aims to discuss Swarm Intelligence approaches for Rule Discovery in Data Mining. The first approach is a new rule learning algorithm based on Particle Swarm optimization (PSO) and that uses a Multiobjective technique to conceive a complete novel approach to induce classifiers, called MOPSO-N. In this approach the properties of the rules can be expressed in different objectives and then the algorithm finds these rules in an unique run by exploring Pareto dominance concepts. The second approach, called PSO/ACO2 algorithm, uses a hybrid technique combining Particle Swarm Optimization and Ant Colony Optimization. Both approaches directly deal with continuous and nominal attribute values, a feature that current bioinspired rule induction algorithms lack. In this work, an experiment is performed to evaluated both approaches by comparing the performance of the induced classifiers.

References

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Paper Citation


in Harvard Style

B. de Carvalho A., Savegnago T. and Pozo A. (2010). SWARM INTELLIGENCE FOR RULE DISCOVERY IN DATA MINING . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 314-319. DOI: 10.5220/0002966303140319


in Bibtex Style

@conference{iceis10,
author={Andre B. de Carvalho and Taylor Savegnago and Aurora Pozo},
title={SWARM INTELLIGENCE FOR RULE DISCOVERY IN DATA MINING},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={314-319},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002966303140319},
isbn={978-989-8425-05-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - SWARM INTELLIGENCE FOR RULE DISCOVERY IN DATA MINING
SN - 978-989-8425-05-8
AU - B. de Carvalho A.
AU - Savegnago T.
AU - Pozo A.
PY - 2010
SP - 314
EP - 319
DO - 10.5220/0002966303140319