Data mining and visualization techniques for high-dimensional data provide helpful information to substantially augment decision-making. Optimization techniques provide a way to efficiently search for these solutions. ACO applied to data mining tasks – a decision tree construction – is one of these methods and the focus of this paper. The Ant Colony Decision Tree (ACDT) approach generates solutions efficiently and effectively but scales poorly to large problems. This article merges the methods that have been developed for better construction of decision trees by ants. The ACDT approach is tested in the context of the bi-criteria evaluation function by focusing on two problems: the size of the decision trees and the accuracy of classification obtained during ACDT performance. This approach is tested in co-learning mechanism, it means agents–ants can interact during the construction decision trees via pheromone values. This cooperation is a chance of getting better results. The proposed methodology of analysis of ACDT is tested in a number of well-known benchmark data sets from the UCI Machine Learning Repository. The empirical results clearly show that the ACDT algorithm creates good solutions which are located in the Pareto front. The software that implements the ACDT algorithm used to generate the results of this study can be downloaded freely from http://www.acdtalgorithm.com. 
Enhancing the effectiveness of Ant Colony Decision Tree algorithms by co-learning