The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, you 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 MULAN framework.

Main developers:

Meka screenshot Meka screenshot

NEW Feb 13, 2015 Meka 1.7.5 is now has been released. Main changes include

  • Usability improvements to the GUI
  • Added javadocs documentation, Updated tutorial
  • Minor bugs fixed and improvements
  • Classifiers added (HASEL, DeepML)

Meka 1.7.5 is on Maven Central. To include it in your projects,


Sep 25, 2014 Meka 1.7.3 is now has been released. Main changes include
  • Evaluation code is now more efficient at working with large ARFF files
  • Several methods (e.g., PS, RandomSubspaceML) and tools (e.g., PSUtils) rewritten to be more scalable for datasets having a large labelset
  • Classifier Chains (CC) based methods (CC, PCC, BCC, MCC) consolidated to share common code
  • Classifiers added (RAkEL, RAkELd)
Code and classifiers from this release were used to help get 1st place in the LSHTC4 2014 challenge and 2nd place in the 2014 WISE challenge (in combination with Antti Puurula's SGM toolkit).


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 including a PhD thesis, they can can be found here.

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


The following datasets have been created / compiled into WEKA's ARFF; They are all text datasets, parsed into binary-attribute format using WEKA's 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

Usage notes: Attributes 1-L of these datasets represent the label space, and other attributes represent the attribute space

Other notes: A greater selection of multi-label datasets can be found at the MULAN Website. The Medical and Ohsumed datasets can be found here.