The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, we want to predict multiple output variables for each input instance. This different from the 'standard' case (binary, or multi-class classification) which involves only a single target variable. MEKA is based on the WEKA Machine Learning Toolkit; it includes dozens of multi-label methods from the scientific literature, as well as a wrapper to the related MULAN framework.

Main developers:

Meka screenshot Meka screenshot

NEW Aug 29, 2015 Meka 1.7.7 is released. Bug fixes, minor improvements and new features in the GUI.

  • A bug which caused an error when using RandomForest as a base classifier, is now fixed.
  • Can now visualize Drawable base classifiers, for example, J48. Just right-click 'Show Graphs' in the GUI results History
  • Improvements to the GUI such as
    • an Open Recent option to the GUI results History
    • a Save Model option to the GUI results History
  • MCC classifier (and derivatives) now run faster in the case that no chain-search is specified
  • OS-specific Meka home directories

May 22, 2015 Some examples of using Meka for data streams (i.e., Updateable classifiers) added to the list of methods.

Meka on Maven Central

To include it in your projects,



Download MEKA here.

Or checkout the code with subversion:
svn checkout svn:// meka-code

Or get a nightly snapshot.


Getting Started: download MEKA and run ./ (run.bat on Windows) to launch the GUI.

The MEKA tutorial (pdf) has numerous examples on how to run and extend MEKA.

A List of Methods available in MEKA, and examples on how to use them.

The API reference.

MEKA originated from implementations of work from several publications.

Have a specific problem or query? Post to MEKA's Mailing List (please avoid contacting developers directly for MEKA-related help).


A collection of multi-label and multi-target datasets is available here. Even more datasets are available at the MULAN Website (note that MULAN indexes labels as the final attributes, whereas MEKA indexs as the beginning). See the MEKA Tutorial for more information.

The following text datasets have been created / compiled into WEKA's ARFF format using the StringToWordVector filter. Also available are train/test splits and the original raw prefiltered text.

Dataset L N LC PU Description and Original Source(s)
Enron 53 1702 3.39 0.442 A subset of the Enron Email Dataset, as labelled by the UC Berkeley Enron Email Analysis Project
Slashdot 22 3782 1.18 0.041 Article titles and partial blurbs mined from
Language Log 75 1460 1.18 0.208 Articles posted on the Language Log
IMDB (Updated) 28 120919 2.00 0.037 Movie plot text summaries labelled with genres sourced from the Internet Movie Database interface, labeled with genres.

N = The number of examples (training+testing) in the datasets

L = The number of predefined labels relevant to this dataset

LC = Label Cardinality. Average number of labels assigned per document

PU = Percentage of documents with Unique label combinations


Other software that uses MEKA

Other multi-label links