Robustness of optimal randomized decision trees with dynamic programming
V.J. Götz (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J.G.M. van der Linden – Mentor (TU Delft - Algorithmics)
Emir Demirović – Mentor (TU Delft - Algorithmics)
Frans A. Oliehoek – Graduation committee member (TU Delft - Interactive Intelligence)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Decision tree learning is widely done heuristically, but advances in the field of optimal decision trees have made them a more prominent subject of research. However, current methods for optimal decision trees tend to overlook the metric of robustness. Our research wants to find out whether the robustness of optimal decision trees can be improved by incorporating randomization. To achieve this, we added randomization to the existing MurTree algorithm, and performed experiments to compare the robustness. The results show that adding randomization improves the robustness of the decision tree but lowers the out of sample accuracy.