lvf                  package:dprep                  R Documentation

_L_a_s _V_e_g_a_s _F_i_l_t_e_r

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

     Las Vegas Filter uses a random generation of subsets and an 
     inconsistency measure as the evaluation function to determine the
     relevance of features.

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

     lvf(data, lambda, maxiter)

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

    data: name of the discretized dataset

  lambda: threshold for the inconsistency

 maxiter: maximum number of iterations

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

     If the dataset has continuous variables, these must first be
     discretized. This package includes four discretization methods. A
     value of lambda close to the  inconsistency of the whole dataset
     yields a large number of selected features, a large lambda yields
     few selected features.

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

bestsubset: the best subset of features 

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

     Edgar Acuna

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

     LIU, H. and SETIONO, R. (1996). A probabilistic approach to
     feature selectiona filter solution. Proc. of the thirteenth
     International Conference of Machine Learning, 319-337.

_S_e_e _A_l_s_o:

     'disc.ew','inconsist','finco'

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

     #---- LVF method ----
     data(my.iris)
     disciris=disc.ew(my.iris,1:6)
     inconsist(disciris)
     lvf(disciris,0,500)

