frbs (version 3.2-0)

# FH.GBML: 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).