Multi-label classifiers and evaluation procedures using the Weka machine learning framework.
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The MEKA project provides an open source implementation of methods for multi-label learning and evaluation.


See for sources of documentation regarding MEKA.

In particular,

If you have a specific question, ask on Meka's mailing list

Changes in Version 1.9.1

  • Added a folder mekaexamples with examples of how to use Meka from Java code
  • Evaluation can handle missing values
  • BR now runs faster on large datasets
  • PCC now outputs probabilistic info (as it should)
  • Bug fix with labelset print-outs in evaluation at particular verbosity levels
  • Classifier BaggingMLUpdateableADWIN removed to free dependence of MOA
  • -T option is now available for incremental classifiers, evaluating the classifier in its current state (or after training with -t finished) on the test set provided with this option.
  • The loading of the test test in the Classify tab got moved into the menu, to make it more obvious.
  • The Classify tab now allows the loading of serialized models and their evaluation against the loaded test set.
  • The Classify tab now allows to make predictions on a loaded test set using the selected model from the result history.
  • The Arff Viewer got renamed to Data Viewer as it is a customized version of Weka's Arff Viewer, with correct visualization of the class attributes (also sports support for recent files and filechooser with directory shortcuts).
  • New classifiers (Boolean Matrix Factorization, Neurofuzzy methods)
  • Added -predictions option to evaluation (batch and incremental) to allow output of predictions generated on test set to a file. Using the -no-eval option, the evaluation can be skipped, e.g., when there are no class labels in the test set.
  • Added an 'Export Predictions (CSV)' plugin option to the GUI to save all predictions along with true label relevances to a CSV file
  • Moved issues in the TODO section of this README to github as Issues

Bugs, and Future Enhancements

A list of current Issues in Meka (known bugs, planned improvements, feature wishlist) can be found at

The Meka developers never have enough time to implement everything that should be in Meka. If you have made some Meka-related code you would like to see in Meka, or would like to help with any of the existing issues, please get in touch with the developers.