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

Jesse Read*, Luca Martino, Pablo M. Olmos, David Luengo

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

44 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2096-2109
Number of pages14
JournalPattern Recognition
Volume48
Issue number6
DOIs
Publication statusPublished - 1 Jun 2015
MoE publication typeA1 Journal article-refereed

Keywords

  • Bayesian networks
  • Classifier chains
  • Multi-label classification
  • Multi-output prediction
  • Structured inference

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