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A hybrid PSO/ACO algorithm for classification

Holden, Nicholas and Freitas, Alex A. (2007) A hybrid PSO/ACO algorithm for classification. In: Yu, Tina, ed. GECCO '07 Proceedings of the 9th annual conference companion on Genetic and evolutionary computation. GECCO Genetic and Evolutionary Computation Conference . ACM, New York, USA, pp. 2745-2750. ISBN 978-1-59593-698-1. (doi:10.1145/1274000.1274080) (KAR id:14572)

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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 choice of rule quality measure greatly effects the end performance of PSO/ACO2. In addition, the results show that PSO/ACO2 is very competitive with respect to two well-known rule induction algorithms.

Item Type: Book section
DOI/Identification number: 10.1145/1274000.1274080
Uncontrolled keywords: particle swarm optimization, data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04 UTC
Last Modified: 16 Nov 2021 09:52 UTC
Resource URI: (The current URI for this page, for reference purposes)
Freitas, Alex A.:
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