Meka

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

http://meka.sourceforge.net/

Using Meka

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

Release Notes, Version 1.9.1

Improvements since the last release, for the up and coming release (several of these thanks to Joerg Wicker):

• Evaluation can handle missing values
• New classifiers
• 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
• ...

TODO

A list of points flagged for improvement in future versions of Meka:

• Add more user control to the -verbosity flag
• Support for multi-target regression
• Add Nemenyi test for latex saver
• Include a confusion matrix in output
• Use 0.5 threshold as default; and force 0.5 (or pre-set ad-hoc) whenever user has not supplied additionally the training set with the load-from-disk option
• Use printf-style printing instead of Utils.doubleToString output throughout
• For incremental evaluation
  • Add option for prequentialbasic, prequentialwindow, window-based
  • Check options for split-percentage and supervision
  • Check, the first window is/may be a different size
  • The trainset/split in the GUI could indicate how much of the data is used for the initial training set
  • Need to change info about window to sampling frequency
  • With Randomize=true -- randomize?
• Update Tutorial with newer references
• Package manager -- with Mulan as a package
• PS should take -P 1 as the default
• Change EnsembleML to Ensemble
• CC
  • Add an option to CNode.java to use the distribution information, rather than the nominal value, as an attribute.
  • use Range to specify a fixed chain in the options
• Add multitarget.RAkELd
• The 'type' (ML,MT,CV) is not a very elegant way to do things
• Check the use of Filters with Meka classifiers
• Wrapper for Clus
• Use a matrix for storing all values in Result (sparse matrix in the case of multi-label).
• Generate Markdown from the classifier code (e.g., the globalInfo, tipText and technical info)
• Better confidence outputs for multi-target methods, the full distribution should be available
• More classifiers!