Learning Decision Trees with Stochastic Linear Classifiers
Proceedings of Algorithmic Learning Theory, PMLR 83:489-528, 2018.
In this work we propose a top-down decision tree learning algorithm with a class of linear classifiers called stochastic linear classifiers as the internal nodes’ hypothesis class. To this end, we derive efficient algorithms for minimizing the Gini index for this class for each internal node, although the problem is non-convex. Moreover, the proposed algorithm has a theoretical guarantee under the weak stochastic hypothesis assumption.