Mandatory Parameters
readers
(DimensionBundle)readerTrain
: name of the reader class for the training datareaderTrainParams
: a list of parameterName-value pairs to configure the training data readerreaderTest
: 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 ofdocument
,unit
,pair
, orsequence
learningMode
: one ofsingleLabel
,multiLabel
, orregression
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 specifiedfeatureMode
)