|
Class Summary |
| AbstractEntropyWeighting |
This operator calculates the relevance of a feature by computing the
an entropy value of the class distribution, if the given example set would
have been splitted according to the feature. |
| AbstractWeighting |
This is an abstract superclass for RapidMiner weighting operators. |
| AttributeWeights2ExampleSet |
This operator creates a new example set from the given attribute weights. |
| BackwardWeighting |
Uses the backward selection idea for the weighting of features. |
| ChiSquaredWeighting |
This operator calculates the relevance of a feature by computing
for each attribute of the input example set the value of the
chi-squared statistic with respect to the class attribute. |
| ComponentWeights |
For models creating components like PCA, GHA
and FastICA you can create the AttributeWeights
from a component. |
| CorpusBasedFeatureWeighting |
This operator uses a corpus of examples to characterize a single class by
setting feature weights. |
| CorrelationWeighting |
This class provides a weighting scheme based upon correlation. |
| EvolutionaryWeighting |
This operator performs the weighting of features with an evolutionary strategies approach. |
| ExampleSet2AttributeWeights |
This operator creates a new attribute weights IOObject from a given example
set. |
| FeatureWeighting |
This operator performs the weighting under the naive assumption that the
features are independent from each other. |
| ForestBasedWeighting |
This weighting schema will use a given random forest to extract the implicit
importance of the used attributes. |
| ForwardWeighting |
This operator performs the weighting under the naive assumption that the
features are independent from each other. |
| GiniWeighting |
This operator calculates the relevance of a feature by computing the
Gini index of the class distribution, if the given example set would
have been splitted according to the feature. |
| InfoGainRatioWeighting |
This operator calculates the relevance of a feature by computing the
information gain ratio for the class distribution (if exampleSet would
have been splitted according to each of the given features). |
| InfoGainWeighting |
This operator calculates the relevance of a feature by computing the information gain in class distribution, if exampleSet would be splitted after
the feature. |
| InteractiveAttributeWeighting |
This operator shows a window with the currently used attribute weights and
allows users to change the weight interactively. |
| NameBasedWeighting |
This operator is able to create feature weights based on regular expressions defined
for the feature names. |
| OneRErrorWeighting |
This operator calculates the relevance of a feature by computing the error
rate of a OneR Model on the exampleSet without this feature. |
| PCAWeighting |
Uses the factors of one of the principal components (default is the first) as
feature weights. |
| ProcessLog2AttributeWeights |
This operator creates attribute weights from an attribute column in the
statistics created by the ProcessLog operator. |
| PSOWeighting |
This operator performs the weighting of features with a particle swarm approach. |
| ReliefWeighting |
Relief measures the relevance of features by sampling examples
and comparing the value of the current feature for the nearest
example of the same and of a different class. |
| SimpleWeighting |
This PopulationOperator realizes a simple weighting, i.e. creates a list of
clones of each individual and weights one attribute in each of the clones
with some different weights. |
| StandardDeviationWeighting |
Creates weights from the standard deviations of all attributes. |
| SVMWeighting |
Uses the coefficients of the normal vector of a linear SVM as feature weights. |
| SymmetricalUncertaintyOperator |
This operator calculates the relevance of an attribute by measuring
the symmetrical uncertainty with respect to the class. |
| VarianceAdaption |
Implements the 1/5-Rule for dynamic parameter adaption of the variance of a
WeightingMutation. |
| WeightingMutation |
Changes the weight for all attributes by multiplying them with a gaussian
distribution. |