Examples (GUI)¶
If you want to use MOAFS
in MOA’s graphical user interface (GUI), you can use the following command from a terminal (Linux/MAC) from the lib
folder
where your MOA is installed:
java -cp moafs.jar:moa.jar -javaagent:sizeofag-1.0.4.jar moa.gui.GUI
Or if you are using Windows:
java -cp .;moafs.jar;moa.jar -javaagent:sizeofag-1.0.4.jar moa.gui.GUI
If everything is OK, MOA’s GUI should appear as illustrated in the figure below.

Classification without feature selection (No method)¶

Click on Configure
button on the left side. The Configure Task
window should appear. Select class moa.tasks.EvaluateInterleavedTestThenTrain
in the
first dropdown list.

Still on the Configure Task
window, on learner
options, click on the Edit
button. The Editing option: learner
window should be presented. Select
class moa.featureselection.classifiers.NaiveBayes
on the first dropdown list. Three options must be presented as ilustrated in the figure below.
With these options, you can select the number of relevant features to be selected, the feature selection method and the processing window size. To perform
classification without feature selection, set fsMethods
to 0.

To select a data set from a local directory, on the Configure Task
window, on stream
options, click on the Edit
button.

Then, Editing option: stream
window should be presented. Select
class moa.streams.ArffFileStream
on the first dropdown list. Then you can select the data set from the arffFile
option. Click OK
and then OK
again.

If everything is according to plan, just press the OK
button on all windows and you will be returned to the main window. There, just click on the Run
button on the right side and MOA
will perform the classification of the data set using the selected feature selection method.

Information Gain¶

Click on Configure
button on the left side. The Configure Task
window should appear. Select class moa.tasks.EvaluateInterleavedTestThenTrain
in the
upper dropdown list.

On the Configure Task
window, on learner
options, click on the Edit
button. The Editing option: learner
window should be presented. Select
class moa.featureselection.classifiers.NaiveBayes
on the first dropdown list.

Select a desired number of features on numFeatures
and the window size on winSize
options. To use Information Gain as a feature selection method,
simply set fsMethod
to 1. If everything is set up accordingly, click OK
.
To select a data set from a local directory, on the Configure Task
window, on stream
options, click on the Edit
button.

Then, Editing option: stream
window should be presented. Select
class moa.streams.ArffFileStream
on the first dropdown list. Then you can select the data set from the arffFile
option. Click OK
and then OK
again.

You will return to the main page. There, just click on the Run
button on the right side and MOA
will perform the classification of the data set using Information Gain as the selected feature selection method.

Symmetrical Uncertainty¶

Click on Configure
button on the left side. The Configure Task
window should appear. Select class moa.tasks.EvaluateInterleavedTestThenTrain
in the
upper dropdown list.

On the Configure Task
window, on learner
options, click on the Edit
button. The Editing option: learner
window should be presented. Select
class moa.featureselection.classifiers.NaiveBayes
on the first dropdown list.

Select a desired number of features on numFeatures
and the window size on winSize
options. To use Symmetrical Uncertainty as a feature selection method,
simply set fsMethod
to 2. If everything is set up accordingly, click OK
.
To select a data set from a local directory, on the Configure Task
window, on stream
options, click on the Edit
button.

Then, Editing option: stream
window should be presented. Select
class moa.streams.ArffFileStream
on the first dropdown list. Then you can select the data set from the arffFile
option. Click OK
and then OK
again.

You will return to the main page. There, just click on the Run
button on the right side and MOA
will perform the classification of the data set using Information Gain as the selected feature selection method.

Chi-Squared¶

Click on Configure
button on the left side. The Configure Task
window should appear. Select class moa.tasks.EvaluateInterleavedTestThenTrain
in the
upper dropdown list.

On the Configure Task
window, on learner
options, click on the Edit
button. The Editing option: learner
window should be presented. Select
class moa.featureselection.classifiers.NaiveBayes
on the first dropdown list.

Select a desired number of features on numFeatures
and the window size on winSize
options. To use Chi-Squared as a feature selection method,
simply set fsMethod
to 3. If everything is set up accordingly, click OK
.
To select a data set from a local directory, on the Configure Task
window, on stream
options, click on the Edit
button.

Then, Editing option: stream
window should be presented. Select
class moa.streams.ArffFileStream
on the first dropdown list. Then you can select the data set from the arffFile
option. Click OK
and then OK
again.

You will return to the main page. There, just click on the Run
button on the right side and MOA
will perform the classification of the data set using Chi-Squared as the selected feature selection method.

Cramers V-Test¶

Click on Configure
button on the left side. The Configure Task
window should appear. Select class moa.tasks.EvaluateInterleavedTestThenTrain
in the
upper dropdown list.

On the Configure Task
window, on learner
options, click on the Edit
button. The Editing option: learner
window should be presented. Select
class moa.featureselection.classifiers.NaiveBayes
on the first dropdown list.

Select a desired number of features on numFeatures
and the window size on winSize
options. To use Cramers V-Test as a feature selection method,
simply set fsMethod
to 4. If everything is set up accordingly, click OK
.
To select a data set from a local directory, on the Configure Task
window, on stream
options, click on the Edit
button.

Then, Editing option: stream
window should be presented. Select
class moa.streams.ArffFileStream
on the first dropdown list. Then you can select the data set from the arffFile
option. Click OK
and then OK
again.

You will return to the main page. There, just click on the Run
button on the right side and MOA
will perform the classification of the data set using Cramers V-Test as the selected feature selection method.

Gain Ratio¶

Click on Configure
button on the left side. The Configure Task
window should appear. Select class moa.tasks.EvaluateInterleavedTestThenTrain
in the
upper dropdown list.

On the Configure Task
window, on learner
options, click on the Edit
button. The Editing option: learner
window should be presented. Select
class moa.featureselection.classifiers.NaiveBayes
on the first dropdown list.

Select a desired number of features on numFeatures
and the window size on winSize
options. To use Gain Ratio as a feature selection method,
simply set fsMethod
to 5. If everything is set up accordingly, click OK
.
To select a data set from a local directory, on the Configure Task
window, on stream
options, click on the Edit
button.

Then, Editing option: stream
window should be presented. Select
class moa.streams.ArffFileStream
on the first dropdown list. Then you can select the data set from the arffFile
option. Click OK
and then OK
again.

You will return to the main page. There, just click on the Run
button on the right side and MOA
will perform the classification of the data set using Gain Ratio as the selected feature selection method.

Extremal Feature Selection¶

Click on Configure
button on the left side. The Configure Task
window should appear. Select class moa.tasks.EvaluateInterleavedTestThenTrain
in the
upper dropdown list.

On the Configure Task
window, on learner
options, click on the Edit
button. The Editing option: learner
window should be presented. Select
class moa.featureselection.classifiers.NaiveBayes
on the first dropdown list.

Select a desired number of features on numFeatures
and the window size on winSize
options. To use Extremal Feature Selection as a feature selection method,
simply set fsMethod
to 6. If everything is set up accordingly, click OK
.
To select a data set from a local directory, on the Configure Task
window, on stream
options, click on the Edit
button.

Then, Editing option: stream
window should be presented. Select
class moa.streams.ArffFileStream
on the first dropdown list. Then you can select the data set from the arffFile
option. Click OK
and then OK
again.

You will return to the main page. There, just click on the Run
button on the right side and MOA
will perform the classification of the data set using Extremal Feature Selection as the selected feature selection method.

Online Feature Selection¶

Click on Configure
button on the left side. The Configure Task
window should appear. Select class moa.tasks.EvaluateInterleavedTestThenTrain
in the
upper dropdown list.

On the Configure Task
window, on learner
options, click on the Edit
button. The Editing option: learner
window should be presented. Select
class moa.featureselection.classifiers.NaiveBayes
on the first dropdown list.

Select a desired number of features on numFeatures
and the window size on winSize
options. To use online Feature Selection as a feature selection method,
simply set fsMethod
to 7. If everything is set up accordingly, click OK
.
To select a data set from a local directory, on the Configure Task
window, on stream
options, click on the Edit
button.

Then, Editing option: stream
window should be presented. Select
class moa.streams.ArffFileStream
on the first dropdown list. Then you can select the data set from the arffFile
option. Click OK
and then OK
again.

You will return to the main page. There, just click on the Run
button on the right side and MOA
will perform the classification of the data set using Online Feature Selection as the selected feature selection method.
