reduce: Reduce the size of rule base

View source: R/reduce.R

reduceR Documentation

Reduce the size of rule base


From given rule base, select such set of rules that influence mostly the rule base coverage of the input data.


  tnorm = c("goedel", "goguen", "lukasiewicz"),
  tconorm = c("goedel", "goguen", "lukasiewicz"),
  numThreads = 1



Data for the rules to be evaluated on. Could be either a numeric matrix or numeric vector. If matrix is given then the rules are evaluated on rows. Each value of the vector or column of the matrix represents a predicate - it's numeric value represents the truth values (values in the interval [0, 1]).


Either an object of class "farules" or list of character vectors where each vector is a rule with consequent being the first element of the vector. Elements of the vectors (predicate names) must correspond to the x's names (of columns if x is a matrix).


A percentage of rule base coverage that must be preserved. It must be a value within the [0, 1] interval. Value of 1 means that the rule base coverage of the result must be the same as coverage of input rules. A sensible value is e.g. 0.9.


Which t-norm to use as a conjunction of antecedents. The default is "goedel".


Which t-norm to use as a disjunction, i.e. to combine multiple antecedents to get coverage of the rule base. The default is "goedel".


How many threads to use for computation. Value higher than 1 causes that the algorithm runs in several parallel threads (using the OpenMP library).


From a given rulebase, a rule with greatest coverage is selected. After that, additional rules are selected that increase the rule base coverage the most. Addition stops after the coverage exceeds original coverage * ratio.

Note that the size of the resulting rule base is not necessarily minimal because the algorithm does not search all possible combination of rules. It only finds a local minimum of rule base size.


Function returns an instance of class farules() or a list depending on the type of the rules argument.


Michal Burda


M. Burda, M. Štěpnička, Reduction of Fuzzy Rule Bases Driven by the Coverage of Training Data, in: Proc. 16th World Congress of the International Fuzzy Systems Association and 9th Conference of the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT 2015), Advances in Intelligent Systems Research, Atlantic Press, Gijon, 2015.

See Also

rbcoverage(), farules()

lfl documentation built on Sept. 8, 2022, 5:08 p.m.

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