This is the internal function that implements the fuzzy rulebased classification
system with weight factor (FRBCS.W). It is used to solve classification tasks.
Users do not need to call it directly,
but just use frbs.learn
and predict
. This method is
suitable only for classification problems.
1 2  FRBCS.W(range.data, data.train, num.labels, num.class, type.mf,
type.tnorm = "MIN", type.snorm = "MAX", type.implication.func = "ZADEH")

range.data 
a matrix (2 \times n) containing the range of the normalized data, where n is the number of variables, and first and second rows are the minimum and maximum values, respectively. 
data.train 
a matrix (m \times n) of normalized data for the training process, where m is the number of instances and n is the number of variables; the last column is the output variable. Note the data must be normalized between 0 and 1. 
num.labels 
a matrix (1 \times n), whose elements represent the number of labels (linguistic terms); n is the number of variables. 
num.class 
an integer number representing the number of labels (linguistic terms). 
type.mf 
the type of the shape of the membership functions. 
type.tnorm 
the type of tnorm. See 
type.snorm 
the type of snorm. See 
type.implication.func 
the type of implication function. See 
This method is adopted from Ishibuchi and Nakashima's paper. Each fuzzy IFTHEN rule consists of antecedent linguistic values and a single consequent class with certainty grades (weights). The antecedent part is determined by a gridtype fuzzy partition from the training data. The consequent class is defined as the dominant class in the fuzzy subspace corresponding to the antecedent part of each fuzzy IFTHEN rule and the certainty grade is calculated from the ratio among the consequent class. A class of the new instance is determined by the consequent class of the rule with the maximal product of the compatibility grade and the certainty grade.
H. Ishibuchi and T. Nakashima, "Effect of rule weights in fuzzy rulebased classification systems", IEEE Transactions on Fuzzy Systems, vol. 1, pp. 59  64 (2001).
FRBCS.eng
, frbs.learn
, and predict
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