Eiben, E orcid.org/0000-0003-1935-651X, Ganian, R, Kanj, I et al. (2 more authors) (2023) The Computational Complexity of Concise Hypersphere Classification. In: Proceedings of Machine Learning Research. ICML 2023 Fortieth International Conference on Machine Learning, 23-29 Jul 2023, Honolulu, Hawai. Proceedings of the 40th International Conference on Machine Learning, 202 . PMLR , pp. 9060-9070.
Abstract
Hypersphere classification is a classical and foundational method that can provide easy-to-process explanations for the classification of real-valued as well as binary data. However, obtaining an (ideally concise) explanation via hypersphere classification is much more difficult when dealing with binary data as opposed to real-valued data. In this paper, we perform the first complexity-theoretic study of the hypersphere classification problem for binary data. We use the fine-grained parameterized complexity paradigm to analyze the impact of structural properties that may be present in the input data as well as potential conciseness constraints. Our results include not only stronger lower bounds but also a number of new fixed-parameter algorithms for hypersphere classification of binary data, which can find an exact and concise explanation when one exists.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of an article published in the Proceedings of Machine Learning Research. |
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: | 31 May 2023 10:59 |
Last Modified: | 17 Dec 2023 15:36 |
Published Version: | https://proceedings.mlr.press/faq.html |
Status: | Published |
Publisher: | PMLR |
Series Name: | Proceedings of the 40th International Conference on Machine Learning |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199664 |