LkbGBMLMichigan             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 Michigan genetic method. This is a
     genetic algorithm where each individual represents a rule. With
     the given probability, the crossing operation is performed by
     swapping two partition labels between two individuals. With the
     given probability, the mutation is performed by with a 50% of
     probability changing to a random appropriate value each of the
     partitions labels. "label" means a reference to a division in the
     partition, coded as a positive integer. This two operations are
     done the number of iterations specified. This makes the population
     grow and diversify. The final result is a knowledge base made by
     gathering in a base the actual pupulation of rules. Described in
     chapter 5, pages 105-117 at Ishibuchi et al.$

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

      LkbGBMLMichigan(P, gen=1000, cross=0.9, muta=0.01,  train)

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

     Takes the vector of partitions, the number of generations, the 
     crossing probability, the mutation probability and the train data.

       P: The vector of partitions.

     gen: The number of generations.

   cross: The cross probability up to 1.

    muta: The mutation probability up to 1.

   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)
      LkbGBMLMichigan(P,1000,0.9,0.01,trainA)

