MEKA: A multi-label/multi-target extension to WEKA

Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes

Research output: Contribution to journalArticleScientificpeer-review

137 Citations (Scopus)
50 Downloads (Pure)


Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-known WEKA library. MEKA provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.

Original languageEnglish
Article number21
Pages (from-to)1-5
JournalJournal of Machine Learning Research
Publication statusPublished - 1 Feb 2016
MoE publication typeA1 Journal article-refereed


  • Classification
  • Incremental
  • Learning
  • Multi-label
  • Multi-target


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