LkbGBMLPittsBurgh            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 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.$

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

      LkbGBMLPittsBurgh(P, gene=50, cross=0.9, muta=0.8, 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 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.

_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)
      LkbGBMLPittsBurgh(P,1000,0.9,0.8,trainA)

