Ant Colony Optimization
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
On the quest for optimal rule learning heuristics
Machine Learning
Editorial survey: swarm intelligence for data mining
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Evaluating the use of different measure functions in the predictive quality of ABC-miner
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Ant colony optimization (ACO) algorithms for classification in general employ a sequential covering strategy to create a list of classification rules. A key component in this strategy is the selection of the rule quality function, since the algorithm aims at creating one rule at a time using an ACO-based procedure to search the best rule. Recently, an improved strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules instead of individual rules. In the cAnt-MinerPB algorithm, the rule quality function has a smaller role and the search is guided by the quality of a list of rules. This paper sets out to determine the effect of different rule and list quality functions in terms of both predictive accuracy and size of the discovered model in cAnt-MinerPB. The comparative analysis is performed using 12 data sets from the UCI Machine Learning repository and shows that the effect of the rule quality functions in cAnt-MinerPB is different from the results previously presented in the literature.