choice {relations} | R Documentation |
Choose objects based on an ensemble of relations between these.
relation_choice(x, method = "symdiff", weights = 1, control = list(), ...)
x |
an ensemble of endorelations. |
method |
a character string specifying one of the built-in methods, or a function to be taken as a user-defined method. See Details for available built-in methods. |
weights |
a numeric vector with non-negative case weights.
Recycled to the number of elements in the ensemble given by |
control |
a list of control parameters. See Details. |
... |
a list of control parameters (overruling those specified
in |
A social choice function is a rule for choosing from a set D of objects, i.e., selecting suitable subsets of D. Voting rules used in elections are the most prominent example of such functions, which typically aggregate individual preferences (e.g., of voters).
Choice methods "symdiff"
, "CKS"
and "euclidean"
choose a given number k of objects (“winners”) by
determining a relation R minimizing ∑_b w_b d(R_b, R)^p
over all relations for which winners are always strictly preferred to
losers, without any further constraints on the relations between pairs
of winners or pairs of losers, where d is symmetric difference
(symdiff, “Kemeny-Snell”), Cook-Kress-Seiford (CKS), or
Euclidean dissimilarity, respectively, and w_b is the case
weight given to R_b. For symdiff and CKS choice, the R_b
must be crisp endorelations, and p = 1; for Euclidean choice,
the R_b can be crisp or fuzzy endorelations, and p = 2.
(Note that solving such a choice problem is different from computing
consensus preference relations.)
Available control options include:
k
an integer giving the number of objects/winners to be chosen.
n
the maximal number of optimal choices to be
obtained, with NA
constants or "all"
indicating to
obtain all optimal choices. By default, only a single optimal
choice is computed.
Choice method "Schulze"
implements the Schulze method for
selecting winners from (votes expressing) preferences. See e.g.
http://en.wikipedia.org/wiki/Schulze_method for details.
Currently, the Schulze heuristic is used, and the set of all possible
winners is returned.
A set with the chosen objects, or a list of such sets.
data("SVM_Benchmarking_Classification") ## Determine the three best classification learners in the above sense. relation_choice(SVM_Benchmarking_Classification, k = 3)