Mandatory Parameters

• readers (DimensionBundle)
  • readerTrain: name of the reader class for the training data
  • readerTrainParams: a list of parameterName-value pairs to configure the training data reader
  • readerTest: name of the reader class for the test data (not necessary for Crossvalidation)
  • readerTestParams: a list of parameterName-value pairs to configure the test data reader (not necessary for Crossvalidation)
• featureSet: a list of feature extractor class names (the feature extractors to use)
• pipelineParameters: a list of parameterName-value pairs (parameters necessary to configure the feature extractors)
• dataWriter: a DataWriter class (a writer to produce the input for the classification framework, e.g. Weka)
• classificationArguments: a Weka/Meka classifier class and list of arguments to parametrize it (the classification algorithm)
• featureMode: one of document, unit, pair, or sequence
• learningMode: one of singleLabel, multiLabel, or regression

Optional Parameters

• threshold: boolean (the threshold to create a bipratition from a ranking; only for multiLabel learning mode)
• featureSelection (DimensionBundle)
  • attributeEvaluator: Weka attribute selection evaluation class and list of arguments to parametrize it (the attribute evaluation algorithm)
  • featureSearcher: Weka attribute selection search class and list of arguments to parametrize it (the ranking algorithm; only for singleLabel learning mode)
  • labelTransformationMethod: a Mulan label transformation method (the label transformation method; only for multiLabel learning mode)
  • numLabelsToKeep: integer (the number of features which will be selected; only for multiLabel learning mode)
  • applySelection: boolean (whether to actually apply the )
• developerMode: boolean (if true, you will not be warned when using feature extractors incompatible with the specified featureMode)