Class and Description |
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ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
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Class and Description |
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NominalToBinary
Converts all nominal attributes into binary numeric attributes.
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Normalize
Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
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ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
|
Class and Description |
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Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
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MakeIndicator
A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
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Normalize
Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
|
Remove
A filter that removes a range of attributes from the dataset.
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RemoveUseless
This filter removes attributes that do not vary at all or that vary too much.
|
Class and Description |
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Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
|
MultiInstanceToPropositional
Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.
Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId. |
NominalToBinary
Converts all nominal attributes into binary numeric attributes.
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PropositionalToMultiInstance
Converts the propositional instance dataset into multi-instance dataset (with relational attribute).
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ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
|
Class and Description |
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Remove
A filter that removes a range of attributes from the dataset.
|
Class and Description |
---|
ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
|
Class and Description |
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ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
|
Class and Description |
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AddExpression
An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
|
Class and Description |
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AbstractTimeSeries
An abstract instance filter that assumes instances form time-series data and
performs some merging of attribute values in the current instance with
attribute attribute values of some previous (or future) instance.
|
Center
Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
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Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
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NominalToBinary
Converts all nominal attributes into binary numeric attributes.
|
PotentialClassIgnorer
This filter should be extended by other unsupervised attribute
filters to allow processing of the class attribute if that's
required.
|
Remove
A filter that removes a range of attributes from the dataset.
|
ReplaceMissingValues
Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
|
Standardize
Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
|
TimeSeriesTranslate
An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
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