EnsembleSelection (3.7)

Combines several classifiers using the ensemble selection method

For more information, see: Caruana, Rich, Niculescu, Alex, Crew, Geoff, and Ksikes, Alex, Ensemble Selection from Libraries of Models, The International Conference on Machine Learning (ICML'04), 2004.Implemented in Weka by Bob Jung and David Michael.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


EnsembleSelection Options

L: Specifies the Model Library File, continuing the list of all models.

W: Specifies the Working Directory, where all models will be stored.

B: Set the number of bags, i.e., number of iterations to run the ensemble selection algorithm.

E: Set the ratio of library models that will be randomly chosen to populate each bag of models.

V: Set the ratio of the training data set that will be reserved for validation.

H: Set the number of hillclimbing iterations to be performed on each model bag.

I: Set the the ratio of the ensemble library that the sort initialization algorithm will be able to choose from while initializing the ensemble for each model bag

X: Sets the number of cross-validation folds.

P: Specify the metric that will be used for model selection during the hillclimbing algorithm. Valid metrics are: accuracy, rmse, roc, precision, recall, fscore, all

A: Specifies the algorithm to be used for ensemble selection. Valid algorithms are: "forward" (default) for forward selection. "backward" for backward elimination. "both" for both forward and backward elimination. "best" to simply print out top performer from the ensemble library "library" to only train the models in the ensemble library

R: Flag whether or not models can be selected more than once for an ensemble.

G: Whether sort initialization greedily stops adding models when performance degrades.

O: Flag for verbose output. Prints out performance of all selected models.

S: Random number seed. (default 1)

D: If set, classifier is run in debug mode and may output additional info to the console

Select target column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Numeric class] Dependencies: [] min # Instance: 1

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

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Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


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