Timely Classification of Hierarchical Classes
Tarek F. Abdelzaher, Sanjoy Baruah, Alan Burns, Yigong Hu
摘要
AbstractÐ 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 (ªI Don't Knowº) 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).
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