Abstract
Ant-Miner is an ant-based algorithm for the discovery of classification rules. This paper proposes five extensions to Ant-Miner: (1) we utilize multiple types of pheromone, one for each permitted rule class, i.e. an ant first selects the rule class and then deposits the corresponding type of pheromone; (2) we use a quality contrast intensifier to magnify the reward of high-quality rules and to penalize low-quality rules in terms of pheromone update; (3) we allow the use of a logical negation operator in the antecedents of constructed rules; (4) we incorporate stubborn ants, an ACO variation in which an ant is allowed to take into consideration its own personal past history; (5) we use an ant colony behavior in which each ant is allowed to have its own values of the α and β parameters (in a sense, to have its own personality). Empirical results on 23 datasets show improvements in the algorithm’s performance in terms of predictive accuracy and simplicity of the generated rule set.
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Salama, K.M., Abdelbar, A.M. & Freitas, A.A. Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm. Swarm Intell 5, 149–182 (2011). https://doi.org/10.1007/s11721-011-0057-9
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DOI: https://doi.org/10.1007/s11721-011-0057-9