Scalable multi-output label prediction: From classifier chains to classifier trellises Articles uri icon

publication date

  • June 2015

start page

  • 2096

end page

  • 2109


  • 6


  • 48

International Standard Serial Number (ISSN)

  • 0031-3203


  • Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modelling a fully cascaded chain. In particular, the methods' strategies for discovering and modelling a good chain structure constitute a major computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels. (C) 2015 Elsevier Ltd. All rights reserved.


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