Multi-label classifiers and evaluation procedures using the Weka machine learning framework.
java.lang.Exception: Illegal options: ...
exception, which affected all general options when executing algorithms from the command-line. This was due to a recent change in Weka, which now always performs a check for remaining options. See https://github.com/Waikato/meka/issues/74.MULAN
classifier now has an renamedAttributes
property which determines whether to rename attributes (for avoiding invalid characters); on by default (as it was current behavior); command-line flag -no-rename
turns it off.props
files are now read from the MEKA home directory as well ($HOME/.meka
or %USERPROFILE%\mekafiles
)-x-out-dir
command-line option for storing the per-fold data of a cross-validation runThe MEKA project provides an open source implementation of methods for multi-label learning and evaluation.
See http://meka.sourceforge.net/#documentation for sources of documentation regarding MEKA.
In particular,
Tutorial.pdf
for detailed information on obtaining, using and extending MEKA.If you have a specific question, ask on Meka's mailing list
mekaexamples
with examples of how to use Meka from Java codeBR
now runs faster on large datasetsPCC
now outputs probabilistic info (as it should)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.-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.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.
See the Tutorial.pdf for detailed information on obtaining, using and extending MEKA. For a list of included methods and 'quick-start' command line examples, see: http://meka.sourceforge.net/methods.html
Improvements since the last version are as follows.
MEKA's build has been switched over from Apache Ant to Apache Maven.
The Evaluation framework has been heavily reworked
doubles[]
can be stored in Results
, rather than just Strings
and Doubles
.The seed used to randomize a dataset is no longer passed on to Randomizeable
classifiers -- they must use their own.
Randomizeable
classifiers will be different to earlier versions of MEKA when a dataset is randomized
(of course, the result should not be statistically significant).It is easier now to add new functionality to Result History objects.
meka.gui.explorer.classify.AbstractClassifyResultHistoryPlugin
and placed in the meka.gui.explorer.classify
package.The Explorer tabs are now plugins and get discovered dynamically at runtime.
meka.gui.explorer.AbstractExplorerTab
and placed in the meka.gui.explorer
package.A GUIChooser class is now available: meka.gui.guichooser.GUIChooser
meka.gui.guichooser.AbstractMenuItemDefinition
and placed in package meka.gui.guichooser
.isShortcutButton()
method return true.Meka now has an Experimenter
ExperimentExample.java
for an example of how to do this on the command line.The MultilabelClassifier
class has been (more appropriately) renamed ProblemTransformationMethod
, and there is now a MultiLabelClassifier
Interface.
MajorityLabelsetClassifier
now implement MultilabelClassifier
. Most others are ProblemTransformationMethod
sTool tips and get/set options thoroughly elabourated throughout classifiers, and respective javadoc comments cleaned up
Tutorial updated to reflect changes
A number of minor bug fixes, e.g.,
PSt
when empty labelset appearsSNN
where also fixedSee the Tutorial.pdf for detailed information on obtaining, using and extending MEKA. For a list of included methods and 'quick-start' command line examples, see: http://meka.sourceforge.net/methods.html
This is a minor release, adding new features and fixing some bugs, including:
- Fixed a bug which caused Meka to crash when using RandomForest as a base classifier
- Can now visualize certain base classifiers, for example, J48. Just right-click 'Show Graphs' in the GUI results History
- Other improvements to the GUI such as
- an Open Recent option
- a Save Model option to the GUI results History
- MCC classifier (and derivatives) now run faster in the case that no chain-search is made
- OS-specific Meka home directories
- Recent changes are reflected in the tutorial
See the Tutorial.pdf for detailed information on obtaining, using and extending MEKA. For a list of included methods and 'quick-start' command line examples, see: http://meka.sourceforge.net/methods.html
This is a minor release, fixing a few minor issues.
- Updateable classifiers are now moved to subfolders incremental/ and incremental/meta
- Updateable classifiers are now set with a sensible default classifier (HoeffdingTree), and BRUpdateable in the case of meta incremental classifiers
- Javadoc comments are cleaned up
- Some unused branches of weka/ and moa/ were removed
- Some overly stringent unit tests were changed
- Recent changes are reflected in the tutorial