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Full-class set classification using the Hungarian algorithm

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Abstract

Consider a set-classification task where c objects must be labelled simultaneously in c classes, knowing that there is only one object coming from each class (full-class set). Such problems may occur in automatic attendance registration systems, simultaneous tracking of fast moving objects and more. A Bayes-optimal solution to the full-class set classification problem is proposed using a single classifier and the Hungarian assignment algorithm. The advantage of set classification over individually based classification is demonstrated both theoretically and experimentally, using simulated, benchmark and real data.

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Notes

  1. The relevance of the logarithm will transpire later in relation to the Bayes optimality of the set classifier. The base of the logarithm can be any.

  2. The Matlab code for the Hungarian algorithm was written by Alex Melin, University of Tennessee, 2006, available through Matlab Central.

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  1. School of Computer Science, Bangor University, Bangor, Gwynedd, LL57 1UT, UK

    Ludmila I. Kuncheva

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  1. Ludmila I. Kuncheva
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Kuncheva, L.I. Full-class set classification using the Hungarian algorithm. Int. J. Mach. Learn. & Cyber. 1, 53–61 (2010). https://doi.org/10.1007/s13042-010-0002-z

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