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
Campagner, A., Ciucci, D. (2018). Three-Way and Semi-supervised Decision Tree Learning Based on Orthopartitions. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations (pp.748-759) [10.1007/978-3-319-91476-3_61].
Three-Way and Semi-supervised Decision Tree Learning Based on Orthopartitions
Campagner, A;Ciucci, D
2018
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
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