Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Swarm intelligence
Ant Colony Optimization
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Facing classification problems with Particle Swarm Optimization
Applied Soft Computing
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
A hybrid PSO/ACO algorithm for classification
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
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
Classification rule discovery with DE/QDE algorithm
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Novel swarm optimization for mining classification rules on thyroid gland data
Information Sciences: an International Journal
Multi-Objective ant programming for mining classification rules
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
A memetic approach for the knowledge extraction
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
Immune ant swarm optimization for optimum rough reducts generation
International Journal of Hybrid Intelligent Systems
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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.