Class Summary |
AbstractTimeSeriesFilter |
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. |
AddFilter |
An instance filter that adds a new attribute to the dataset.
|
AllFilter |
A simple instance filter that passes all instances directly
through. |
AttributeExpressionFilter |
Applys a mathematical expression involving attributes and numeric
constants to a dataset. |
AttributeFilter |
An instance filter that deletes a range of attributes from the dataset. |
AttributeSelectionFilter |
Filter for doing attribute selection. |
AttributeTypeFilter |
An instance filter that deletes all attributes of a specified type
from the dataset. |
CopyAttributesFilter |
An instance filter that copies a range of attributes in the dataset.
|
DiscretizeFilter |
An instance filter that discretizes a range of numeric attributes in
the dataset into nominal attributes. |
DynamicReductDiscretize |
|
EmptyAttributeFilter |
Removes all attributes that do not contain more than one distinct
value. |
Filter |
An abstract class for instance filters: objects that take instances
as input, carry out some transformation on the instance and then
output the instance. |
FirstOrderFilter |
This instance filter takes a range of N numeric attributes and replaces
them with N-1 numeric attributes, the values of which are the difference
between consecutive attribute values from the original instance. eg:
Original attribute values
0.1, 0.2, 0.3, 0.1, 0.3
New attribute values
0.1, 0.1, 0.1, -0.2, -0.2
The range of attributes used is taken in numeric order. |
InstanceFilter |
Filters instances according to the value of an attribute. |
MakeIndicatorFilter |
Creates a new dataset with a boolean attribute replacing a nominal
attribute. |
MergeTwoValuesFilter |
Merges two values of a nominal attribute. |
NominalToBinaryFilter |
Converts all nominal attributes into binary numeric
attributes. |
NonSparseToSparseFilter |
A filter that converts all incoming instances into sparse format. |
NormalizationFilter |
Normalizes all numeric values in the given dataset. |
NullFilter |
A simple instance filter that allows no instances to pass
through. |
NumericToBinaryFilter |
Converts all numeric attributes into binary attributes (apart from
the class attribute): if the value of the numeric attribute is
exactly zero, the value of the new attribute will be zero. |
NumericTransformFilter |
Transforms numeric attributes using a
given transformation method. |
ObfuscateFilter |
A simple instance filter that renames the relation, all attribute names
and all nominal (and string) attribute values. |
RandomizeFilter |
This filter randomly shuffles the order of instances passed through it.
|
ReplaceMissingValuesFilter |
Replaces all missing values for nominal and numeric attributes in a
dataset with the modes and means from the training data. |
ResampleFilter |
Produces a random subsample of a dataset. |
RSDiscretize |
|
RSNumerizeNominal |
Creates numerical attributes of nominal ones, in case we wish to
discretize them also |
RSReplaceMissingValues |
Replaces all missing values for nominal and numeric attributes in a
dataset with the modes and means from the training data. |
SparseToNonSparseFilter |
A filter that converts all incoming sparse instances into
non-sparse format. |
SplitDatasetFilter |
This filter takes a dataset and outputs a subset of it. |
SpreadSubsampleFilter |
Produces a random subsample of a dataset. |
StringToNominalFilter |
Converts a string attribute (i.e. unspecified number of values) to nominal
(i.e. set number of values). |
SwapAttributeValuesFilter |
Swaps two values of a nominal attribute. |
TimeSeriesDeltaFilter |
An instance filter that assumes instances form time-series data and
replaces attribute values in the current instance with the difference
between the current value and the equivalent attribute attribute value
of some previous (or future) instance. |
TimeSeriesTranslateFilter |
An instance filter that assumes instances form time-series data and
replaces attribute values in the current instance with the equivalent
attribute attribute values of some previous (or future) instance. |