Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery

The cAnt-Miner algorithm is an Ant Colony Optimization (ACO) based technique for classification rule discovery in problem domains which include continuous attributes. In this paper, we propose several extensions to cAntMiner. The main extension is based on the use of multiple pheromone types, one for each class value to be predicted. In the proposed μcAnt-Miner algorithm, an ant first selects a class value to be the consequent of a rule and the terms in the antecedent are selected based on the pheromone levels of the selected class value; pheromone update occurs on the corresponding pheromone type of the class value. The pre-selection of a class value also allows the use of more precise measures for the heuristic function and the dynamic discretization of continuous attributes, and further allows for the use of a rule quality measure that directly takes into account the confidence of the rule. Experimental results on 20 benchmark datasets show that our proposed extension improves classification accuracy to a statistically significant extent compared to cAnt-Miner, and has classification accuracy similar to the well-known Ripper and PART rule induction algorithms.