TkbGBMLMoErrorSize           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 multiobjective, Error Size Algorith
     genetic method. It is a multiobjective version of the Error Size
     algorithm. Instead of a single fitness function, there are two.
     The fitness functions take in consideration the train error and
     the number of rules (size). The first fitness function would be
     higher as there are fewer wrong infered cases (error) in training.
     The second function would be higher as there are fewer rules in
     the knowledge base. As it is multiobjective it returns a set of
     knowledge bases. Described in chapter 5, pages 127-130 at
     Ishibuchi et al.$

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

      TkbGBMLMoErrorSize(kB, gen=100, cross=0.5, muta=0.01, train, 
                           k=0.01, popu=20)

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

     Takes knowledge base, the number of generations, the  crossing and
     mutation probability, the train data, the size weight  and the
     initial population.

      kB: The knowledge base to tweak.

     gen: The number of generations.

   cross: The cross probability up to 1.

    muta: The mutation probability up to 1.

   train: The train dataset.

       k: The weight of the size in the algorithm.

    popu: The initial population of the algorithm.

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

     Returns the set of not dominated knowledge bases.

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

      data(kB)
      data(trainA)
      TkbGBMLMoErrorSize(kB, 50, 0.8,0.01, trainA,0.01, 20)

