This is the internal function that implements the fuzzy
rule-based 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.
a matrix(2 x 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 x n) of 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.
label.inp
a matrix(1 x n) whose elements represent
the number of labels (fuzzy terms), where n is the number
of variables.
num.class
an integer number representing the
number of labels (fuzzy terms).
type.mf
the type of the shape of the membership
functions.
Details
This method is adopted from Hisao Ishibuchi and Tomoharu
Nakashima's paper. Each fuzzy IF-THEN rule consists of
antecedent linguistic values and a single consequent
class with certainty grades (weights). The antecedent
part is determined by a grid-type 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 IF-THEN 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.
References
H. Ishibuchi and T. Nakashima, "Effect of rule weights in
fuzzy rule-based classification systems", IEEE
Transactions on Fuzzy Systems, vol. 1, pp. 59 - 64
(2001).