Dabrowski, K.K., Eiben, E., Ordyniak, S. orcid.org/0000-0003-1935-651X et al. (2 more authors) (2024) Learning Small Decision Trees for Data of Low Rank-Width. In: Wooldridge, M., Dy, J. and Natarajan, S., (eds.) Proceedings of the 38th AAAI Conference on Artificial Intelligence. Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), 20-27 Feb 2024, Vancouver, Canada. AAAI Press , Washington, DC, USA , pp. 10476-10483. ISBN 978-1-57735-887-9
Abstract
We consider the NP-hard problem of finding a smallest decision tree representing a classification instance in terms of a partially defined Boolean function. Small decision trees are desirable to provide an interpretable model for the given data. We show that the problem is fixed-parameter tractable when parameterized by the rank-width of the incidence graph of the given classification instance. Our algorithm proceeds by dynamic programming using an NLC decomposition obtained from a rank-width decomposition. The key to the algorithm is a succinct representation of partial solutions. This allows us to limit the space and time requirements for each dynamic programming step in terms of the parameter.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). This is an author produced version of a conference paper published in Proceedings of the AAAI Conference on Artificial Intelligence. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | KRR: 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: | 19 Jan 2024 14:00 |
Last Modified: | 17 May 2024 16:01 |
Status: | Published |
Publisher: | AAAI Press |
Identification Number: | 10.1609/aaai.v38i9.28916 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208014 |