TkbAnalyticL              package:FKBL              R Documentation

_T_w_e_a_k_s _a _k_B

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

     This algorithm is called Analytic. It is the same as TkbAnalytic,
     but with the parameters packed in a list.  It needs the
     TkbAnalyticP 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
     for the cases whose class wasn't correctly inferred, it tries to
     improve the rule set. This improvement is done by examining three
     different solutions. First, lowering the winning rule's weight so
     it is no longer the winning rule in this situation. Second, find a
     non winning rule which wouldn't have made a mistake, and rise its
     weight up to the value which makes it to win. Third, leave it as
     is. The situation which gives less error with the whole training
     set, would be the chosen one. This algorithm is described in
     chapter 3, pages 048-057 at Ishibuchi et al.$

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

      TkbAnalyticL(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
           TkbAnaliticP.

   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<-TkbAnalyticP(kB, 1000, 0.1)
      data(trainA)
      TkbAnalyticL(Params, trainA)

