Fuzzy 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.

Input Ports

  1. Type: Data Numeric data as well as class information used for training.

Output Ports

  1. Type: Data Rules with fuzzy intervals in each dimension, classification columns, and additional rule measures.
  2. Type: Fuzzy Basis Function Fuzzy Rule Model can be used for prediction.

Find here

Analytics > Mining > Rule Induction > Fuzzy Rules

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