|
Class Summary |
| 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. |
| Add |
An instance filter that adds a new attribute to the dataset.
|
| AddCluster |
A filter that adds a new nominal attribute representing the cluster assigned
to each instance by the specified clustering algorithm. |
| AddExpression |
Applys a mathematical expression involving attributes and numeric
constants to a dataset. |
| AddNoise |
Introduces noise data a random subsample of the dataset
by changing a given attribute
(attribute must be nominal)
Valid options are:
-C col
Index of the attribute to be changed. |
| ClusterMembership |
A filter that uses a clusterer to obtain cluster membership probabilites
for each input instance and outputs them as new instances. |
| Copy |
An instance filter that copies a range of attributes in the dataset.
|
| Discretize |
An instance filter that discretizes a range of numeric attributes in
the dataset into nominal attributes. |
| FirstOrder |
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. |
| MakeIndicator |
Creates a new dataset with a boolean attribute replacing a nominal
attribute. |
| MergeTwoValues |
Merges two values of a nominal attribute. |
| NominalToBinary |
Converts all nominal attributes into binary numeric attributes. |
| Normalize |
Normalizes all numeric values in the given dataset. |
| NumericToBinary |
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. |
| NumericTransform |
Transforms numeric attributes using a
given transformation method. |
| Obfuscate |
A simple instance filter that renames the relation, all attribute names
and all nominal (and string) attribute values. |
| PKIDiscretize |
Discretizes numeric attributes using equal frequency binning where the
number of bins is equal to the square root of the number of non-missing
values. |
| PotentialClassIgnorer |
This filter should be extended by other unsupervised attribute
filters to allow processing of the class attribute if that's
required. |
| RandomProjection |
Reduces the dimensionality of the data by projecting
it onto a lower dimensional subspace using a random
matrix with columns of unit length (It will reduce
the number of attributes in the data while preserving
much of its variation like PCA, but at a much less
computational cost). |
| Remove |
An instance filter that deletes a range of attributes from the dataset. |
| RemoveType |
A filter that removes attributes of a given type. |
| RemoveUseless |
This filter removes attributes that do not vary at all or that vary too much.
|
| 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.
|
| StringToNominal |
Converts a string attribute (i.e. unspecified number of values) to nominal
(i.e. set number of values). |
| StringToWordVector |
Converts String attributes into a set of attributes representing word
occurrence information from the text contained in the strings. |
| SwapValues |
Swaps two values of a nominal attribute. |
| TimeSeriesDelta |
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. |
| TimeSeriesTranslate |
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. |