# FH.GBML: FH.GBML model building In frbs: Fuzzy Rule-Based Systems for Classification and Regression Tasks

## 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

 1 2 3 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).

frbs documentation built on Dec. 16, 2019, 1:19 a.m.