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)

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
Article number21
Pages (from-to)1-5
JournalJournal of Machine Learning Research
Volume17
Publication statusPublished - 1 Feb 2016
MoE publication typeA1 Journal article-refereed

Keywords

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

Fingerprint

Dive into the research topics of 'MEKA: A multi-label/multi-target extension to WEKA'. Together they form a unique fingerprint.

Cite this