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

  • Jesse Read
  • , Peter Reutemann
  • , Bernhard Pfahringer
  • , Geoff Holmes
  • Aalto University
  • University of Waikato

Research output: Contribution to journalArticlepeer-review

220 Citations (Scopus)

Abstract

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
JournalJournal of Machine Learning Research
Volume17
Publication statusPublished - 1 Feb 2016
Externally publishedYes

Keywords

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

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