Journal
PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17)
Volume -, Issue -, Pages 433-440Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3071178.3071306
Keywords
Ant Colony Optimization; data mining; classification rules; sequential covering
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Ant-Miner Mixed Attributes (Ant-Miner(MA)) was inspired and built based on ACO(MV), which uses an archive-based pheromone model to cope with mixed attribute types. On the one hand, the use of an archive-based pheromone model improved significantly the runtime of Ant-Miner(MA) and helped to eliminate the need for discretisation procedure when dealing with continuous attributes. On the other hand, the graph-based pheromone model showed superiority when dealing with datasets containing a large size of attributes, as the graph helps the algorithm to easily identify good attributes. In this paper, we propose an automatic design framework to incorporate the graph-based model along with the archive-based model in the rule creation process. We compared the automatically designed hybrid algorithm against existing ACO-based algorithms: one using a graph-based pheromone model and one using an archive-based pheromone model. Our results show that the hybrid algorithm improves the predictive quality over both the base archive-based and graph-based algorithms.
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