FH.GBML {frbs}

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:

• Step 1: Generate population where each individual in the population is a fuzzy rule set.

• Step 2: Calculate the fitness value of each rule set in the current population.

• Step 3: Generate new rule sets by the selection, crossover, and mutation in the same manner as the Pittsburgh-style algorithm. Then, apply iterations of the GCCL to each of the generated rule sets with a probability.

• Step 4: Add the best rule set in the current population to newly generated rule sets to form the next population.

• Step 5: Return to Step 2 if the prespecified stopping condition is not satisfied.

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).