Helal, A. and Otero, F. (2017). Automatic design of ant-miner mixed attributes for classification rule discovery. in:Genetic and Evolutionary Computation (GECCO 2017).ACM, pp. 433-440. Available at: http://dx.doi.org/10.1145/3071178.3071306.
Ant-Miner Mixed Attributes (Ant-MinerMA) was inspired and built based on ACOMV. 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-MinerMA 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.
Helal, A. and Otero, F. (2016). A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm. in:Genetic and Evolutionary Computation Conference (GECCO 2016).ACM Press, pp. 13-20. Available at: http://dx.doi.org/10.1145/2908812.2908900.
In this paper, we introduce Ant-MinerMA to tackle mixed-attribute classification problems. Most classification problems involve continuous, ordinal and categorical attributes. The majority of Ant Colony Optimization (ACO) classification algorithms have the limitation of being able to handle categorical attributes only, with few exceptions that use a discretisation procedure when handling continuous attributes either in a preprocessing stage or during the rule creation. Using a solution archive as a pheromone model, inspired by the ACO for mixed-variable optimization (ACO-MV), we eliminate the need for a discretisation procedure and attributes can be treated directly as continuous, ordinal, or categorical. We compared the proposed Ant-MinerMA against cAnt-Miner, an ACO-based classification algorithm that uses a discretisation procedure in the rule construction process. Our results show that Ant-MinerMA achieved significant improvements on computational time due to the elimination of the discretisation procedure without affecting the predictive performance.