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),
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.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
A zipped version of the software site can be downloaded here.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to email@example.com, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.