Abstract
In this chapter we analyze adaptive and self-adaptive methods for improving performance of ant colony decision trees and forests. Our goal is to present and compare ensemble approaches based on Ant Colony Optimization. The ACDF ensemble (consisting of homogeneous classifiers) described in this chapter is self-adaptive to the analyzed data sets and is characterized by good classification accuracy.
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Kozak, J. (2019). Adaptive Ant Colony Decision Forest 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_8
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DOI: https://doi.org/10.1007/978-3-319-93752-6_8
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