Random k-labelsets for multilabel classification

Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas

Research output: Contribution to journalArticlepeer-review

519 Citations (Scopus)

Abstract

A simple yet effective multilabel learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases, the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called RAkEL (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicates that RAkEL manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multilabel learning methods.

Original languageEnglish
Article number5567103
Pages (from-to)1079-1089
Number of pages11
JournalIEEE Transactions on Knowledge and Data Engineering
Volume23
Issue number7
DOIs
Publication statusPublished - 31 Oct 2011

Keywords

  • Categorization
  • classification
  • ensembles
  • labelset
  • multilabel

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