SVMBench              package:relations              R Documentation

_S_V_M _B_e_n_c_h_m_a_r_k_i_n_g _D_a_t_a _a_n_d _C_o_n_s_e_n_s_u_s _R_e_l_a_t_i_o_n_s

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

     'SVM_Benchmarking_Classification' and
     'SVM_Benchmarking_Regression' represent the results of a benchmark
     study comparing Support Vector Machines to other predictive
     methods on real and artificial data sets involving classification
     and regression methods, respectively.
     'SVM_Benchmarking_Classification_Consensus' and
     'SVM_Benchmarking_Regression_Consensus' are consensus rankings
     derived from these data.

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

     data("SVM_Benchmarking_Classification")
     data("SVM_Benchmarking_Regression")
     data("SVM_Benchmarking_Classification_Consensus")
     data("SVM_Benchmarking_Regression_Consensus")

_F_o_r_m_a_t:

     'SVM_Benchmarking_Classification' ('SVM_Benchmarking_Regression')
     is an ensemble of 21 (12) relations representing pairwise
     comparisons of 17 classification (10 regression) methods on 21
     (12) data sets. Each relation of the ensemble summarizes the
     results for a particular data set. The relations are endorelations
     on the set of methods employed. Since some methods did fail on
     some data sets, the relations are not guaranteed to be complete,
     transitive, or even reflexive. See Meyer et al. (2003) for details
     on the experimental design of the benchmark study, and Hornik et
     Meyer (2007) for the pairwise comparisons.

     'SVM_Benchmarking_Classification_Consensus' and
     'SVM_Benchmarking_Regression_Consensus' are lists of ensembles of
     consensus relations fitted to the benchmark results. For each of
     the following three endorelation families: 'SD/L' ("linear
     orders"), 'SD/O' ("partial orders"), and 'SD/P' ("preferences"),
     _all_ possible consensus relations have been computed (see
     'relation_consensus'). For both classification and regression, the
     three relation ensembles obtained are provided as a named list of
     length 3. See Hornik et Meyer (2007) for details on the
     meta-analysis.

_S_o_u_r_c_e:

     D. Meyer, F. Leisch, and K. Hornik (2003), The support vector
     machine under test. _Neurocomputing_, *55*:169-186.

     K. Hornik and D. Meyer (2007), Deriving consensus rankings from
     benchmarking experiments. In R. Decker and H.-J. Lenz, _Advances
     in Data Analysis_. Studies in Classification, Data Analysis, and
     Knowledge Organization. Springer-Verlag.

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

     data("SVM_Benchmarking_Classification")

     ## 21 data sets
     names(SVM_Benchmarking_Classification)

     ## 17 methods
     relation_domain(SVM_Benchmarking_Classification)

     ## select preferences
     P <- sapply(SVM_Benchmarking_Classification, relation_is_preference)

     ## only the artifical data sets yield preferences
     names(SVM_Benchmarking_Classification)[P]

     ## visualize them using Hasse diagrams
     if (require("Rgraphviz"))
         plot(SVM_Benchmarking_Classification[P])

     ## Same for regression:
     data("SVM_Benchmarking_Regression")

     ## 12 data sets
     names(SVM_Benchmarking_Regression)

     ## 10 methods
     relation_domain(SVM_Benchmarking_Regression)

     ## select preferences
     P <- sapply(SVM_Benchmarking_Regression, relation_is_preference)

     ## only two of the artifical data sets yield preferences
     names(SVM_Benchmarking_Regression)[P]

     ## visualize them using Hasse diagrams
     if (require("Rgraphviz"))
         plot(SVM_Benchmarking_Regression[P])

     ## Consensus solutions:

     data("SVM_Benchmarking_Classification_Consensus")
     data("SVM_Benchmarking_Regression_Consensus")

     ## The solutions for the three families are not unique
     print(SVM_Benchmarking_Classification_Consensus)
     print(SVM_Benchmarking_Regression_Consensus)

     ## visualize the consensus preferences
     if (require("Rgraphviz")) {
         plot(SVM_Benchmarking_Classification_Consensus$P)
         plot(SVM_Benchmarking_Regression_Consensus$P)
     }

     ## in tabular style:
     ranking <- function(x) rev(names(sort(relation_class_ids(x))))
     sapply(SVM_Benchmarking_Classification_Consensus$P, ranking)
     sapply(SVM_Benchmarking_Regression_Consensus$P, ranking)

     ## (prettier and more informative:)
     relation_classes(SVM_Benchmarking_Classification_Consensus$P[[1]])

