This is the internal function that implements the fuzzy
rule-based classification system using Chi's technique
(FRBCS.CHI). 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 was proposed by Zheru Chi, Hong Yan, and Tuan
Pham that extends Wang and Mendel's method. The method
consists of the following five steps:
Step 1: Fuzzify the input space.
Step 2: Generate
fuzzy rules from given training data pairs.
Step 3:
Assign a degree to each rule.
Step 4: Create a
combined rule bank.
Step 5: Determine the mapping
by using a defuzzification method.
References
Z. Chi, H. Yan, T. Pham, "Fuzzy algorithms with
applications to image processing and pattern
recognition", World Scientific, Singapore (1996).