Collective data mining in the ant colony decision tree approach

This paper considers the topic of cooperation between agent ants in an Ant Colony Optimization (ACO) algorithm that is used to construct decision trees (Ant Colony Decision Tree or ACDT). To follow a suitable methodology, the paper presents a formal definition of the ACDT algorithm with a focus on the influence that Ant Colony Optimization algorithms have on the obtained results. The aim of this paper is to provide the rationale for using swarm intelligence (i.e., ACO) in the process of constructing decision trees. Many experiments were conducted to provide a solid justification. These experiments tested cooperation between agent ants in ant colony algorithms with different ACO performance scenarios: the application of only a pheromone trail, the application of only a heuristic function, the application of both components, and the application of neither component. Additionally, different values of the pheromone trail were tested at various stages of the algorithm's operation and pheromone representations were presented. The experiments were conducted on 30 publicly available data sets; all observations were preceded by statistical tests. This paper confirms that it is reasonable to use the pheromone trail and balanced heuristics. Moreover, we found that, for the ACDT algorithm, good results can also be obtained without heuristics.