The MEKA project provides an open source implementation of methods for multi-label classification and evaluation. It is based on the WEKA Machine Learning Toolkit. Several benchmark methods are also included, as well as the pruned sets and classifier chains methods, other methods from the scientific literature, and a wrapper to the MULAN framework.
*NEW* Sep 25, 2014 Meka 1.7.3 is now has been released. Main changes include
- Evaluation code is now a bit faster for working with large ARFF files
- Several methods (e.g., PS, RandomSubspaceML) and tools (e.g., PSUtils) updated to be more scalable with datasets having a large labelset
- Classifier Chains (CC) methods (CC,PCC,BCC,MCC) consolidated to share common code
- Classifiers added (RAkEL,RAkELd)
May 27, 2014 Meka 1.6.2 is now on Maven Central. To include it in your projects,
Download MEKA here.
Or checkout the code with subversion:
svn checkout svn://svn.code.sf.net/p/meka/code/trunk meka-code
Or get a nightly snapshot.
Getting Started: download MEKA and run
bash run.sh (
run.bat on Windows) .
Have a look at the MEKA tutorial with numerous examples on how to run and extend MEKA.
For developers: The API reference.
MEKA began implementating 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 (highly preferable to contacting the developers directly).
|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 Slashdot.org|
|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