Ordyniak, S orcid.org/0000-0003-1935-651X and Szeider, S (2021) Parameterized Complexity of Small Decision Tree Learning. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence - AAAI-21 Technical Tracks 7. Thirty-Fifth AAAI Conference on Artificial Intelligence, 02-09 Feb 2021, Virtual. , pp. 6454-6462. ISBN 978-1-57735-866-4
Abstract
We study the NP-hard problem of learning a decision tree (DT) of smallest depth or size from data. We provide the first parameterized complexity analysis of the problem and draw a detailed parameterized complexity map for the natural parameters: size or depth of the DT, maximum domain size of all features, and the maximum Hamming distance between any two examples. Our main result shows that learning DTs of smallest depth or size is fixed-parameter tractable (FPT) parameterized by the combination of all three of these parameters. We contrast this FPT-result by various hardness results that underline the algorithmic significance of the considered parameters.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Computational Complexity of Reasoning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/V00252X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Jul 2021 14:49 |
Last Modified: | 06 Aug 2021 10:38 |
Published Version: | https://ojs.aaai.org/index.php/AAAI/article/view/1... |
Status: | Published |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176333 |