sffs                  package:dprep                  R Documentation

_S_e_q_u_e_n_t_i_a_l _F_l_o_a_t_i_n_g _F_o_r_w_a_r_d _M_e_t_h_o_d

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

     This function selects features using the sequential  floating
     forward method with lda, knn or rpart classifiers.

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

     sffs(data, method = c("lda", "knn", "rpart"), kvec = 5, 
     repet = 10)

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

    data: dataset to be used for feature selection

  method: string sequence representing the choice of classifier

    kvec: the number of nearest neighbors to be used for the knn
          classifier

   repet: integer value representing the number of repetitions

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

     The Sequential Floating Forward selection method was introduced to
     deal deal with the nesting problem. The best subset of features is
     initialized as the empty set and at each step a new feature is
     added. After that, the  algorithm searches for features that can
     be removed from T until the correct classification error does not
     increase. This algorithm is a combination of the sequential
     forward and the sequential backward methods. The "best subset" of
     features is constructed based on the frequency  with which each
     attribute is selected in the number of repetitions given. Due to
     the time complexity of the algorithm its use is not recommended
     for a large number of attributes(say more than 1000).

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

 fselect: a list of the indices of the best features

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

     Edgar Acuna

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

     Pudil, P., Ferri, J., Novovicov, J., and Kittler, J. (1994). 
     Floating search methods for feature selection with nonmonotonic
     criterion  function. 12 International Conference on Pattern
     Recognition, 279-283. 

     Acua, E , (2003) A comparison of filters and wrappers for feature
     selection in supervised classification. Proceedings of  the
     Interface 2003 Computing Science and Statistics. Vol 34.

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

     #---- SFFS feature selection using the knn classifier ----
     data(my.iris)
     sffs(my.iris,method="knn",kvec=5,repet=5)

