frbs (version 3.2-0)

# GFS.GCCL: GFS.GCCL model building

## Description

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

## Usage

GFS.GCCL(data.train, popu.size = 10, range.data.input, num.labels,
persen_cross = 0.6, persen_mutant = 0.3, max.gen = 10,
range.data.ori)

## 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.

range.data.input

a matrix containing the ranges of the normalized input data.

num.labels

a matrix describing the number of linguistic terms.

persen_cross

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

persen_mutant

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

max.gen

the maximal number of generations for the genetic algorithm.

range.data.ori

a matrix containing the ranges of the input data.

## Details

This method is based on Ishibuchi's method. In this method, a chromosome describes each linguistic IF-THEN rule using integer as its representation of the antecedent part. In the consequent part of the fuzzy rules, the heuristic method is applied to automatically generate the class. The evaluation is calculated for each rule which means that the performance is not based on the entire rule set. The outline of the method is as follows.

• Step 1: Generate an initial population of fuzzy IF-THEN rules.

• Step 2: Evaluate each fuzzy IF-THEN rule in the current population.

• Step 3: Generate new fuzzy IF-THEN rules by genetic operators.

• Step 4: Replace a part of the current population with the newly generated rules.

• Step 5: Terminate the algorithm if a stopping condition is satisfied, otherwise return to Step 2.

Additionally, to handle high dimensional data, this method uses "don't care" attributes on the antecedent fuzzy set.

## References

H. Ishibuchi, T. Nakashima, and T. Murata, "Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems", IEEE trans. on Systems, Man, and Cybernetics - Part B: Sybernetics, vol. 29. no. 5, pp. 601 - 618 (1999).