Abstract
In this chapter we give a detailed description of the most popular ant colony optimization algorithm for learning decision trees. We also present an example which illustrates the main idea of the approach, as well as a detailed discussion on how to apply the pheromone trail in machine learning tasks. Pheromone maps allow for a detailed analysis of how the ant colony decision tree (ACDT) approach works. In the chapter a detailed experimental analysis is also performed, which enables a comparison of the ACDT algorithm with other (classical as well as stochastic) methods.
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Kozak, J. (2019). Ant Colony Decision Tree Approach. In: Decision Tree and Ensemble Learning Based on Ant Colony Optimization. Studies in Computational Intelligence, vol 781. Springer, Cham. https://doi.org/10.1007/978-3-319-93752-6_3
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DOI: https://doi.org/10.1007/978-3-319-93752-6_3
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