FH.GBML {frbs}R Documentation

FH.GBML model building

Description

This is the internal function that implements the Ishibuchi's method based on hybridization of genetic cooperative-competitive learning (GCCL) and Pittsburgh (FH.GBML). It is used to solve classification tasks. Users do not need to call it directly, but just use frbs.learn and predict.

Usage

FH.GBML(data.train, popu.size = 10, max.num.rule = 5,
  persen_cross = 0.6, persen_mutant = 0.3, max.gen = 10, num.class,
  range.data.input, p.dcare = 0.5, p.gccl = 0.5)

Arguments

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.

popu.size

the size of the population which is generated in each generation.

max.num.rule

the maximum number of rules.

persen_cross

a real number between 0 and 1 determining the probability of crossover.

persen_mutant

a real number between 0 and 1 determining the probability of mutation.

max.gen

the maximal number of generations for the genetic algorithms.

num.class

a number of the classes.

range.data.input

a matrix containing the ranges of the normalized input data.

p.dcare

a probability of "don't care" attributes occurred.

p.gccl

a probability of GCCL process occurred.

Details

This method is based on Ishibuchi's method using the hybridization of GCCL and the Pittsburgh approach for genetic fuzzy systems. The algorithm of this method is as follows:

References

H. Ishibuchi, T. Yamamoto, and T. Nakashima, "Hybridization of fuzzy GBML approaches for pattern classification problems," IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 35, no. 2, pp. 359 - 365 (2005).


[Package frbs version 3.2-0 Index]