C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
A new version of the ant-miner algorithm discovering unordered rule sets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
A new classification-rule pruning procedure for an ant colony algorithm
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Classification rule mining with an improved ant colony algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Extensions of ant-miner algorithm to deal with class imbalance problem
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Ant-Miner is an ant-based algorithm for the discovery of classification rules. This paper proposes four extensions to Ant-Miner: 1) we allow the use of a logical negation operator in the antecedents of constructed rules; 2) we use stubborn ants, an ACO-variation in which an ant is allowed to take into consideration its own personal past history; 3) we use multiple types of pheromone, one for each permitted rule class, i.e. an ant would first select the rule class and then deposit the corresponding type of pheromone; 4) we allow each ant to have its own value of the α and β parameters, which in a sense means that each ant has its own individual personality. Empirical results show improvements in the algorithm's performance in terms of the simplicity of the generated rule set, the number of trials, and the predictive accuracy.