Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization

In this paper we introduce a new algorithm, called Ant-Miner-Reg_MA, to tackle the regression problem using an archive-based pheromone model. Existing regression algorithms handle continuous attribute using a discretisation procedure, either in a preprocessing stage or during rule creation. Using an archive as a pheromone model, inspired by the ACO for Mixed-Variable (ACO_MV), we eliminate the need for a discretisation procedure. We compare the proposed Ant-Miner-Reg_MA against Ant-Miner-Reg, an ACO-based regression algorithm that uses a dynamic discretisation procedure, inspired on M5 algorithm, during rule construction process. Our results show that Ant-Miner-Reg_MA achieved a significant improvement in the relative root mean square error of the models created, overcoming the limitations of the dynamic discretisation procedure.