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 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.