LkbGBMLHybrid              package:FKBL              R Documentation

_C_r_e_a_t_e_s _a _k_n_o_w_l_e_d_g_e _b_a_s_e

_D_e_s_c_r_i_p_t_i_o_n:

     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

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

     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.

_V_a_l_u_e:

     Returns a knowledge base with the partitions and the rules.

_S_o_u_r_c_e:

     \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}

_E_x_a_m_p_l_e_s:

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

