From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Swarm intelligence
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
A hybrid PSO/ACO algorithm for discovering classification rules in data mining
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
A PSO/ACO approach to knowledge discovery in a pharmacovigilance context
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
Expert Systems with Applications: An International Journal
AMPSO: a new particle swarm method for nearest neighborhood classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving the performance of hierarchical classification with swarm intelligence
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Hybridisation of particle swarm optimisation with area concentrated search
International Journal of Knowledge-based and Intelligent Engineering Systems
International Journal of Applied Metaheuristic Computing
Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm
Computer Methods and Programs in Biomedicine
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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.