Baruah, Sanjoy, BURNS, ALAN orcid.org/0000-0001-5621-8816, Abdelzaher, Tarek et al. (1 more author) (2025) Timely Classification of Hierarchical Classes. In: RTSS 2025: The 46th IEEE Real-Time Systems Symposium:Proceedings. IEEE.
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: | 17 Sep 2025 12:10 |
Status: | Published |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:231704 |
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