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2018 | OriginalPaper | Chapter

Three-Way and Semi-supervised Decision Tree Learning Based on Orthopartitions

Authors : Andrea Campagner, Davide Ciucci

Published in: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations

Publisher: Springer International Publishing

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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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previous chapter Determining Strategies in Game-Theoretic Shadowed Sets

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Title: Three-Way and Semi-supervised Decision Tree Learning Based on Orthopartitions
Authors: Andrea Campagner
Davide Ciucci
Publisher: Springer International Publishing
Book: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations
Print ISBN: 978-3-319-91475-6

Electronic ISBN: 978-3-319-91476-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91476-3_61

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