A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm

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.