LkbConfidenceSupportL          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 confidence and support method.
     Similar to LkbConfidenceSupport. It needs the
     LkbConfidenceSupporP. It is based in exploring every rule
     possibility, and test its confidence and its support. Support is a
     concept related with the amount of points covered by the rule.
     Confidence is related with the predominant class in the area
     covered by the rule. Only rules with any amount of support are
     included. The inferred class of a rule is set so the confidence is
     maximized.  There is a weight associated with a rule, and there
     are 4 different heuristic methods to obtain it, plus a fifth
     method which simply states an standard value of $\frac{1}{2} $.
     Described in chapter 2, pages 018-025 at Ishibuchi et al.$

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

      LkbConfidenceSupportL(lis,train)

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

     Takes the list of parameters and the train dataset.

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

   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)
      Params<-LkbConfidenceSupportP(P,0)
      data(trainA)
      LkbConfidenceSupportL(Params,trainA)

