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Learning_Optimal_Decision_Trees_BNAIC_FinalVersion.pdf
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Abstract
Decision trees are among the most popular classification models in machine learning. Using greedy algorithms to learn them can pose several disadvantages: it is difficult to limit the size of the decision trees while maintaining a good classification accuracy, and it is hard to impose additional constraints on the models that are learned. For these reasons, there has been a recent interest in exact and flexible algorithms for learning decision trees. This paper is a summary of our paper ”Learn- ing Optimal Decision Trees using Constraint Programming” accepted in CP2019 [4]. In our paper, we introduce a new approach to learn decision trees using constraint programming.
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Verhaeghe, H., Nijssen, S., Pesant, G., Quimper, C.-G., & Schaus, P. (2019). Learning Optimal Decision Trees using Constraint Programming (abstract). Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch C, CEUR Workshop Proceedings(2491), 1. https://hdl.handle.net/2078.5/269956 (Original work published 2019)