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.
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MEKA's build has been switched over from Apache Ant to Apache Maven.
- Note: this change affects people working with the source code.
- It makes life easier with deploying artifacts to Maven Central automatically
- Better execution of unit tests.
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The Evaluation framework has been heavily reworked
- Evaluation output has been improved, as much in the code as the visual text output (now prettier!).
- AUPRC and AUROC are added as evaluation metrics
- AUPRC and AUROC can be visualised with the 'Show ...' options under the right-click menu of the History panel.
- Objects like
doubles[]can be stored inResults, rather than justStringsandDoubles. - In particular there are improvements to cross validation and incremental validation.
- Cross-fold evaluation now combines all predictions together and then evaluates it (rather than averaging the statistics afterwards).
- Incremental evaluation is basic prequential (interleaved train then test) with a GUI option for the number of samples
- Incremental validation displays metrics in the GUI sampled over time in addition to those overall. These can be plotted with by selecting 'Incremental Performance' from the right-click menu in the History panel.
- Note the earlier incremental evaluation scheme (which was window-based prequential) is also still available.
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The seed used to randomize a dataset is no longer passed on to
Randomizeableclassifiers -- they must use their own.- This means that the results of the
Randomizeableclassifiers will be different to earlier versions of MEKA when a dataset is randomized (of course, the result should not be statistically significant).
- This means that the results of the
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It is easier now to add new functionality to Result History objects.
- The Classify tab now automatically discovers its result history plugins at runtime.
- These have to be derived from
meka.gui.explorer.classify.AbstractClassifyResultHistoryPluginand placed in themeka.gui.explorer.classifypackage. - New functionality (Show Graph, Save Graph, Save Model, Save CSV, Incremental Performance, Show ROC, Show PRC, etc.) is using this architecture
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The Explorer tabs are now plugins and get discovered dynamically at runtime.
- This makes it easy for other people to add more tabs (i.e., meka packages), simply derived from
meka.gui.explorer.AbstractExplorerTaband placed in themeka.gui.explorerpackage.
- This makes it easy for other people to add more tabs (i.e., meka packages), simply derived from
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A GUIChooser class is now available:
meka.gui.guichooser.GUIChooser- This allows the selection of either the Explorer (the interface which has existed until now) and the new Experimenter interface.
- It features dynamic discovery of menu items as well:
- They need to be derived from
meka.gui.guichooser.AbstractMenuItemDefinitionand placed in packagemeka.gui.guichooser. - If you want a "shortcut" button like the Explorer menu definition has, simply let the
isShortcutButton()method return true. - See the code for examples.
- They need to be derived from
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Meka now has an Experimenter
- The experimenter is still 'experimental' at the moment.
- It is not based directly on WEKA's experimenter, but should be relatively intuitive to people that have used it.
- See
ExperimentExample.javafor an example of how to do this on the command line. - New documentation on how to use it is in the Tutorial
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The
MultilabelClassifierclass has been (more appropriately) renamedProblemTransformationMethod, and there is now aMultiLabelClassifierInterface.- Methods like
MajorityLabelsetClassifiernow implementMultilabelClassifier. Most others areProblemTransformationMethods
- Methods like
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Tool tips and get/set options thoroughly elabourated throughout classifiers, and respective javadoc comments cleaned up
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Tutorial updated to reflect changes
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A number of minor bug fixes, e.g.,
- bug fixed in
PStwhen empty labelset appears - some related issues in
SNNwhere also fixed
- bug fixed in