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Authors: Andre B. de Carvalho ; Taylor Savegnago and Aurora Pozo

Affiliation: Federal University of Parana, Brazil

Keyword(s): Particle swarm optimization, Rule induction, Multi-objective optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Evolutionary Programming ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

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.

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Paper citation in several formats:
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 5: ICEIS; ISBN 978-989-8425-05-8; ISSN 2184-4992, SciTePress, pages 314-319. DOI: 10.5220/0002966303140319

@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 5: ICEIS},
year={2010},
pages={314-319},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002966303140319},
isbn={978-989-8425-05-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 5: ICEIS
TI - SWARM INTELLIGENCE FOR RULE DISCOVERY IN DATA MINING
SN - 978-989-8425-05-8
IS - 2184-4992
AU - B. de Carvalho, A.
AU - Savegnago, T.
AU - Pozo, A.
PY - 2010
SP - 314
EP - 319
DO - 10.5220/0002966303140319
PB - SciTePress

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