Weight by Relief
Synopsis
This operator calculates the relevance of the attributes by Relief. The key idea of Relief is to estimate the quality of features according to how well their values distinguish between the instances of the same and different classes that are near each other.
Description
Relief is considered one of the most successful algorithms for assessing the quality of features due to its simplicity and effectiveness. The key idea of Relief is to estimate the quality of features according to how well their values distinguish between the instances of the same and different classes that are near each other. 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. This version also works for multiple classes and regression data sets. The resulting weights are normalized into the interval between 0 and 1 if thenormalize weightsparameter is set to true.
Input
example set
This input port expects an ExampleSet. It is output of the Retrieve operator in the attached Example Process.
Output
weights
This port delivers the weights of the attributes with respect to the label attribute. The attributes with higher weight are considered more relevant.
example set
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
Parameters
Normalize weights
This parameter indicates if the calculated weights should be normalized or not. If set to true, all weights are normalized in range from 0 to 1.
Sort weights
This parameter indicates if the attributes should be sorted according to their weights in the results. If this parameter is set to true, the order of the sorting is specified using thedir排序ectionparameter.
Sort direction
This parameter is only available when thesort weightsparameter is set to true. This parameter specifies the sorting order of the attributes according to their weights.
Number of neighbors
This parameter specifies the number of nearest neighbors for relevance calculation.
Sample ratio
This parameter specifies the ratio of examples to be used for determining the weights.
Use local random seed
This parameter indicates if alocal random seedshould be used for randomizing examples of a subset. Using the same value of thelocal random seedwill produce the same sample. Changing the value of this parameter changes the way examples are randomized, thus the sample will have a different set of examples.
Local random seed
This parameter specifies thelocal random seed.这个参数只是如果可用use local random seedparameter is set to true.