Learning Decision Trees with Stochastic Linear Classifiers

Tom Jurgenson, Yishay Mansour

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)489-528
Number of pages40
JournalProceedings of Machine Learning Research
StatePublished - 2018
Event29th International Conference on Algorithmic Learning Theory, ALT 2018 - Lanzarote, Spain
Duration: 7 Apr 20189 Apr 2018


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