From given rule base, select such set of rules that influence mostly the rule base coverage of the input data.
reduce(x, rules, ratio,
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).
Function returns an instance of class farules or a list depending on the type of
the rules argument.
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.
M. Burda, M. <U+0160>t<U+011B>pni<U+010D>ka, 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.