Fuzzy Rule Predictor

This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementFuzzy Rule Predictor

The first port contains the Fuzzy Rule Model that is applied to the test data contained in the second input port. The output data has then one additional column containing the predicted class attribute which is the best match for all rules.

Options

Choose Name
The name of the predicted column appended to the end of the test data.
Don't Know Class
Ignore If selected, no lower degree of class activation is set, otherwise the specified value between 0 and 1 is used.
Default Use the minimum activation threshold from the learning algorithm.
Use Instances where the activation lies above this threshold are classified as a missing (unknown) class. This is useful in cases where the feature space is not completely covered by rules.

Input Ports

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Fuzzy Rule Model to which test data is applied.
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Test data matching the Fuzzy Rule Model structure.

Output Ports

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Predicted data with one additional classification column.

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