relief                 package:dprep                 R Documentation

_R_E_L_I_E_F _F_e_a_t_u_r_e _S_e_l_e_c_t_i_o_n

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

     This function implements the RELIEF feature selection algorithm.

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

     relief(data, nosample, threshold,vnom)

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

    data: the dataset for which feature selection will be carried out

nosample: number of instances drawn from the original dataset

threshold: the cutoff point to select the features

    vnom: a vector containing the indexes of the nominal features

_D_e_t_a_i_l_s:

     The general idea of this method is to choose the features that can
     be most distinguished between classes. These are known as the
     relevant features. At  each step of an iterative process, an
     instance x is chosen at random from the dataset and the weight for
     each feature is updated adcording to the  distance of x to its
     Nearmiss and NearHit.

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

relevant: a table that gives the frequency with which the feature was
          selected as relevant over the trials performed, and the
          average weight of the feature.

  a plot: a plot of the weights of the features

_A_u_t_h_o_r(_s):

     Edgar Acuna

_R_e_f_e_r_e_n_c_e_s:

     KIRA, K. and RENDEL, L. (1992). The Feature Selection Problem :
     Traditional Methods and a new algorithm. Proc. Tenth National
     Conference on Artificial Intelligence, MIT Press, 129-134.

     KONONENKO, I., SIMEC, E., and ROBNIK-SIKONJA, M. (1997).
     Overcoming the myopia of induction learning algorithms with
     RELIEFF. Applied Intelligence Vol7, 1, 39-55.

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

     ##---- Feature Selection ---
     data(my.iris)
     relief(my.iris,150,0.01)

