Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound

Computing an optimal classification tree that provably maximizes training performance within a given size limit, is NP-hard, and in practice, most state-of-the-art methods do not scale beyond computing optimal trees of depth three. Therefore, most methods rely on a coarse binarization of continuous features to maintain scalability. We propose a novel algorithm that optimizes trees directly on the continuous feature data using dynamic programming with branch-and-bound. We develop new pruning techniques that eliminate many sub-optimal splits in the search when similar to previously computed splits and we provide an efficient subroutine for computing optimal depth-two trees. Our experiments demonstrate that these techniques improve runtime by one or more orders of magnitude over state-of-the-art optimal methods and improve test accuracy by 5% over greedy heuristics.

Authors

• Cătălin E. Brița University of Amsterdam Delft University of Technology
• Jacobus G. M. van der Linden Delft University of Technology
• Emir Demirović Delft University of Technology, The Netherlands

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