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2015 | OriginalPaper | Chapter

Multi-classifiers of Small Treewidth

Authors : Arnoud Pastink, Linda C. van der Gaag

Published in: Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Publisher: Springer International Publishing

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Abstract

Multi-dimensional Bayesian network classifiers are becoming quite popular for multi-label classification. These models have the advantage of a high expressive power, but may induce a prohibitively high runtime of classification. We argue that the high runtime burden originates from their large treewidth. Thus motivated, we present an algorithm for learning multi-classifiers of small treewidth. Experimental results show that these models have a small runtime of classification, without loosing accuracy compared to unconstrained multi-classifiers.

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Metadata
Title
Multi-classifiers of Small Treewidth
Authors
Arnoud Pastink
Linda C. van der Gaag
Copyright Year
2015
DOI
https://doi.org/10.1007/978-3-319-20807-7_18

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