TkbRewardPunishmentL          package:FKBL          R Documentation

_R_e_w_a_r_d _a_n_d _P_u_n_i_s_h_m_e_n_t. _T_w_e_a_k_s _a _k_B

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

     This algorithm is called Reward and Punishment.  It is the same as
     TkbRewardPunishment, but with the parameters packed in a list. It
     needs the TkbRewardPunishmentP function to create the list of
     parameters. 

     It takes a knowledge base and tweaks its weights to fit better a
     given train data.  It is based in the idea of checking every
     training case and discover which is the rule used to infer the
     case. If the rule was right, it is rewarded, with an "etaMore"
     value. If the rule did a mistake, it is punished, with an
     "etaLess" value. The reward and the punishment is done by lowering
     or rising the weight associated with a rule. By lowering a weight
     again and again, a rule would have less and less importance. This
     means that rules which make mistakes would appear less and less as
     the winning rules in an Inference one winner method. The same can
     be said with the rising of a rule's weight. The final result, is a
     tweaked rule set, which would be likely more adapted to the actual
     problem. This algorithm is described in chapter 3, pages 039-048
     at Ishibuchi et al.$

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

      TkbRewardPunishmentL(lis, train)

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

     Takes the list of parameters and the train data.

     lis: A list with the arguments of the function. It is created with
          TkbRewardPunishmentP.

   train: The train data.

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

     Returns the tweaked knowledge base.

_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(kB)
      Params<-TkbRewardPunishmentP(kB, 1000, 0.001, 0.1)
      data(trainA)
      TkbRewardPunishmentL(Params,trainA)

