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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

Résultats de recherche: Contribution à un journalArticleRevue par des pairs

Résumé

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.

langue originaleAnglais
journalJournal of Machine Learning Research
Volume17
étatPublié - 1 févr. 2016
Modification externeOui

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