Multi-colony ACO and Rough Set Theory to Distributed Feature Selection Problem

Authors:
Yudel Gómez;Rafael Bello;Ann Nowé;Enrique Casanovas;J. Taminau
Affiliations:
Department of Computer Science, Universidad Central de Las Villas, Cuba;Department of Computer Science, Universidad Central de Las Villas, Cuba;Comp Lab, Department of Computer Science, Vrije Universiteit Brussel, Belgium;Department of Computer Science, Universidad Central de Las Villas, Cuba;Comp Lab, Department of Computer Science, Vrije Universiteit Brussel, Belgium
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Year:
2009

Citing 2
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Ant Colony Optimization

Ant Colony Optimization
A model based on ant colony system and rough set theory to feature selection

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation

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Abstract

In this paper we present a model to distributed feature selection problem (DFSP) based on ACO and RST. The algorithm looks for reducts by using a multi-colony ACO as search method and RST offers the heuristic function to measure the quality of one feature subset. General results of using this approach are shown and formers results of apply ACO and RST to the feature selection problem are referenced.