KNIME Base Nodes version 4.3.2.v202103021015 by KNIME AG, Zurich, Switzerland
This rule learner* learns a Fuzzy Rule Model on labeled numeric data using
Mixed Fuzzy Rule Formation as the underlying training algorithm
(also known as RecBF-DDA algorithm),
see
Influence of fuzzy norms and other heuristics on
"Mixed Fuzzy Rule Formation" for an extension of the algorithm.
This algorithm generates rules based on numeric data, which are
fuzzy intervals in higher dimensional spaces. These
hyper-rectangles are defined by trapezoid fuzzy membership functions for
each dimension. The selected numeric columns of the input data are used
as input data for training and additional columns are used as
classification target, either one column holding the class information
or a number of numeric columns with class degrees between 0 and 1 can
be selected. The data output contains the fuzzy rules after execution.
Each rule consists of one fuzzy interval for each dimension plus
the target classification columns along with a number of rule
measurements. The model output port contains the fuzzy
rule model, which can be used for prediction in the Fuzzy Rule Predictor
node.
(*) RULE LEARNER is a registered trademark of Minitab, LLC and is used with Minitab’s permission.
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