Three-way and semi-supervised decision tree learning based on orthopartitions

Abstract

Decision Tree Learning is one of the most popular machine learning techniques. A common problem with this approach is the inability to properly manage uncertainty and inconsistency in the underlying datasets. In this work we propose two generalized Decision Tree Learning models based on the notion of Orthopair: the first method allows the induced classifiers to abstain on certain instances, while the second one works with unlabeled outputs, thus enabling semi-supervised learning.