LkbGBMLPittsBurgh {FKBL}R Documentation

Creates a knowledge base

Description

This is the implementation of Pittsburgh genetic method. This is a genetic algorithm, where each rule is a set of rules. This algorithm is as the Hybrid algorithm, but PittsBurgh does not use the Michigan algorithm as a mutation operation. Described in chapter 5, pages 117-122 at Ishibuchi et al.\

Usage

 LkbGBMLPittsBurgh(P, gene=50, cross=0.9, muta=0.8, train)

Arguments

Takes the vector of partitions, the number of generations, the crossing and mutation probability in PittsBurgh method and 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.
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)
     LkbGBMLPittsBurgh(P,1000,0.9,0.8,trainA)
    

    [Package FKBL version 0.50-4 Index]