LkbGBMLHybrid {FKBL}R Documentation

Creates a knowledge base

Description

This is the implementation of Hybrid, Pittsburgh-Michigan genetic method. This is a genetic algorithm, each individual represents a set of rules. With the given probability, the crossing operation for an individual is performed by, first selecting two different individuals, and then rule by rule with a 50% of probability, each one is swapped, i.e. every rule has 50

Usage

 LkbGBMLHybrid(P, gene=50, cross=0.9, muta=0.8, crossM=0.9, 
                        mutaM=0.01, replaceH=2, train)

Arguments

Takes the vector of partitions, the number of generations, the crossing and mutation probability in Hybrid method, crossing and mutation probability in the Michigan method, the number of individuals to replace, the train data.

P The vector of partitions.
gene The number of generations.
cross The cross probability up to 1.
muta The mutation probability up to 1.
crossM The cross probability for Michigan up to 1.
mutaM The mutation probability for Michigan up to 1.
replaceH The number of individuals to replace.
train The train dataset.

Value

Returns a knowledge base with the partitions and the rules.

Source

begin{itemize}

  • Ishibuchi, H., Nakashima, T., Nii, M.
  • "Classification and modeling with linguistic information granules."
  • Soft Computing Approaches to Linguistic Data Mining.
  • Springer-Verlag, 2003 end{itemize}

    Examples

     data(P)
     data(trainA)
     LkbGBMLHybrid(P,1000,0.9,0.8,0.9,0.01,2,trainA)
    

    [Package FKBL version 0.50-4 Index]