reduce | R Documentation |
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 )
x |
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]). |
rules |
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 |
ratio |
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
|
tnorm |
Which t-norm to use as a conjunction of antecedents. The
default is |
tconorm |
Which t-norm to use as a disjunction, i.e. to combine
multiple antecedents to get coverage of the rule base. The default is
|
numThreads |
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.
rbcoverage()
, farules()
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