Abdelzaher, Tarek, Baruah, Sanjoy, BURNS, ALAN orcid.org/0000-0001-5621-8816 et al. (1 more author) (2025) Timely Classification of Hierarchical Classes. In: RTSS 2025: The 46th IEEE Real-Time Systems Symposium:Proceedings. IEEE. (In Press)
Abstract
An IDK classifier is a learning-enabled software component that attempts to categorize each input provided to it into one of a fixed set of base classes, returning IDK (“I Don’t Know”) if it is unable to do so to a required level of confidence. We consider the use of IDK classifiers in applications where it is natural to consider the base classes as comprising the leaves of a class hierarchy. Classification into higher levels of such a hierarchy may be easier than classification into base classes. Given a collection of different IDK classifiers that have been trained to classify at different levels of a class hierarchy, we derive algorithms for determining the order in which to use these classifiers so as to minimize the expected duration to successful classification (whilst guaranteeing to meet a hard deadline).
Metadata
| Item Type: | Proceedings Paper |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © IEEE 2025. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
| Dates: |
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| Institution: | The University of York |
| Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
| Depositing User: | Pure (York) |
| Date Deposited: | 17 Sep 2025 12:10 |
| Last Modified: | 23 Sep 2025 09:20 |
| Status: | In Press |
| Publisher: | IEEE |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:231704 |

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