TkbGBMLErrorSize            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 Error Size Algorithm genetic method.
     It is a single objective genetic algorithm. The fitness function
     takes in consideration the train error and the number of rules
     (size). The fitness function would be higher as there are fewer
     wrong inferred cases (error) in training and as there are fewer
     rules in the knowledge base. The "k" parameter is the ponderation
     between the error and the size. 

     $$ fitness = w_1 * error + w_2 * size $$ $$ fitness = error + k *
     size$$ $$ k=  \frac{w_2}{w_1} $$

     This parameter makes the algorithm single objective, so it returns
     only one single knowledge base. This knowledge base would have a
     higher error, but a smaller number of rules. A smaller number of
     rules is desired to make the knowledge base more understandable
     for the user.

     Described in chapter 5, pages 127-130 at Ishibuchi et al.$

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

      TkbGBMLErrorSize(kB, gen=50, cross=0.8, muta=0.01, 
             k=0.01, train, 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.

       k: The weight of the size in the algorithm.

   train: The train dataset.

    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)
      TkbGBMLErrorSize(kB, 50, 0.8,0.01, 0.01, trainA, 20)

