Learning Optimal Decision Trees Using Caching Branch-and-Bound Search


  • Gaël Aglin UCLouvain
  • Siegfried Nijssen UCLouvain
  • Pierre Schaus UCLouvain




Several recent publications have studied the use of Mixed Integer Programming (MIP) for finding an optimal decision tree, that is, the best decision tree under formal requirements on accuracy, fairness or interpretability of the predictive model. These publications used MIP to deal with the hard computational challenge of finding such trees. In this paper, we introduce a new efficient algorithm, DL8.5, for finding optimal decision trees, based on the use of itemset mining techniques. We show that this new approach outperforms earlier approaches with several orders of magnitude, for both numerical and discrete data, and is generic as well. The key idea underlying this new approach is the use of a cache of itemsets in combination with branch-and-bound search; this new type of cache also stores results for parts of the search space that have been traversed partially.




How to Cite

Aglin, G., Nijssen, S., & Schaus, P. (2020). Learning Optimal Decision Trees Using Caching Branch-and-Bound Search. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3146-3153. https://doi.org/10.1609/aaai.v34i04.5711



AAAI Technical Track: Machine Learning