Learning Decision Trees with Stochastic Linear Classifiers
Abstract
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.
Cite
Text
Jurgenson and Mansour. "Learning Decision Trees with Stochastic Linear Classifiers." Proceedings of Algorithmic Learning Theory, 2018.Markdown
[Jurgenson and Mansour. "Learning Decision Trees with Stochastic Linear Classifiers." Proceedings of Algorithmic Learning Theory, 2018.](https://mlanthology.org/alt/2018/jurgenson2018alt-learning/)BibTeX
@inproceedings{jurgenson2018alt-learning,
title = {{Learning Decision Trees with Stochastic Linear Classifiers}},
author = {Jurgenson, Tom and Mansour, Yishay},
booktitle = {Proceedings of Algorithmic Learning Theory},
year = {2018},
pages = {489-528},
volume = {83},
url = {https://mlanthology.org/alt/2018/jurgenson2018alt-learning/}
}