Performs attribute selection on the incoming data.
In case of input in form of a class adams.flow.container.WekaTrainTestSetContainer object, the train and test sets stored in the container are being used.
NB: In case of cross-validation no reduced or transformed data can get generated!

Additional information

Flow input/output:
- input: weka.core.Instances, adams.flow.container.WekaTrainTestSetContainer
- output: adams.flow.container.WekaAttributeSelectionContainer

Container information:
- adams.flow.container.WekaTrainTestSetContainer:
   - Train: training set; weka.core.Instances
   - Test: test set; weka.core.Instances
   - Seed: seed value; java.lang.Long
   - FoldNumber: current fold (1-based); java.lang.Integer
   - FoldCount: total number of folds; java.lang.Integer
   - Train original indices: original indices (0-based, train); array of int
   - Test original indices: original indices (0-based, test); array of int
- adams.flow.container.WekaAttributeSelectionContainer:
   - Train: training set; weka.core.Instances
   - Reduced: reduced dataset; weka.core.Instances
   - Transformed: transformed dataset (if weka.attributeSelection.AttributeTransformer); weka.core.Instances
   - Test: test set; weka.core.Instances
   - Test reduced: reduced test dataset; weka.core.Instances
   - Test transformed: transformed test dataset (if weka.attributeSelection.AttributeTransformer); weka.core.Instances
   - Evaluation: attribute selection evaluation object; weka.attributeSelection.AttributeSelection
   - Statistics: spreadsheet with the statistics;
   - Selected attributes: range string of selected attributes (1-based indices); java.lang.String
   - Seed: seed value (cross-validation); java.lang.Long
   - FoldCount: fold (cross-validation); java.lang.Integer