The MEKA project provides an open source implementation of methods for multi-label learning and evaluation.

http://meka.sourceforge.net/

See http://meka.sourceforge.net/#documentation for sources of documentation regarding MEKA.

In particular,

• See the Tutorial.pdf for detailed information on obtaining, using and extending MEKA.
• For a list of included methods and command line examples for them, see: http://meka.sourceforge.net/methods.html
• For examples on how to use MEKA in your Java code: https://github.com/Waikato/meka/tree/master/src/main/java/mekaexamples

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

• Check if it is already answered: http://sourceforge.net/mailarchive/forum.php?forum_name=meka-list
• Write it to [email protected]

• 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

A list of current Issues in Meka (known bugs, planned improvements, feature wishlist) can be found at https://github.com/Waikato/meka/issues

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.