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GECCO
2007
Springer

A hybrid PSO/ACO algorithm for classification

9 years 5 months ago
A hybrid PSO/ACO algorithm for classification
In a previous work we have proposed a hybrid Particle Swarm Optimisation/Ant Colony Optimisation (PSO/ACO) algorithm for the discovery of classification rules, in the context of data mining. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into numbers in a pre-processing phase. The design of this hybrid algorithm was motivated by the fact that nominal attributes are common in data mining, but the algorithm can in principle be applied to other kinds of problems involving nominal variables (though this paper focuses only on data mining). In this paper we propose several modifications to the original PSO/ACO algorithm. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 16 public-domain real-world datasets often used to benchmark the performance of classification algorithms. PSO/ACO2 is evaluated with two different rule quality (particle "fitness") functions. We show that the ...
Nicholas Holden, Alex Alves Freitas
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Nicholas Holden, Alex Alves Freitas
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