We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal attribute values, a feature that current PSO and ACO rule induction algorithms lack. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 27 public-domain, real-world data sets often used to benchmark the performance of classification algorithms. We compare the PSO/ACO2 algorithm to an industry standard algorithm PART and compare a reduced version of our PSO/ACO2 algorithm, coping only with continuous data, to our new classification algorithm for continuous data based on differential evolution. The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and that PSO/ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining—where the goal is to discover knowledge that is not only accurate but also comprehensible to the user. The results also show that the reduced PSO version for continuous attributes provides a slight increase in accuracy when compared to the differential evolution variant.

This paper proposes a hybrid PSO/ACO algorithm for hierarchical classification, where the classes to be predicted are arranged in a tree-like hierarchy. The performance of the algorithm is evaluated on a challenging biological data set, involving the hierarchical functional classification of enzymes. The proposed algorithm is compared with an existing PSO for classification, which was also adapted for hierarchical classification.

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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.

Abstract. This paper utilizes Ant-Miner -the first Ant Colony algorithm for discovering classification rules -in the field of web content mining, and shows that it is more effective than C5.0 in two sets of BBC and Yahoo web pages used in our experiments. It also investigates the benefits and dangers of several linguistics-based text preprocessing techniques to reduce the large numbers of attributes associated with web content mining.