ls.sorting.treatment {kappalab} | R Documentation |
This function assigns alternatives to classes and optionally compares
the obtained classification to a given one. The classes are
described by a set of prototypes (well-known alternatives for the
decision maker) and a capacity (which can, for instance, be determined by
ls.sorting.capa.ident
). This function (in combination with
ls.sorting.capa.ident
) is an
implementation of the TOMASO method; see Meyer and Roubens (2005).
ls.sorting.treatment(P, cl.proto, a, A, cl.orig.A = NULL)
P |
Object of class |
cl.proto |
Object of class |
a |
Object of class |
A |
Object of class |
cl.orig.A |
Object of class |
The function returns a list structured as follows:
correct.A |
Object of class |
class.A |
Object of class |
eval.correct |
Object of class |
minmax.P |
Object of class |
Choquet.A |
Object of class |
P. Meyer, M. Roubens (2005), Choice, Ranking and Sorting in Fuzzy Multiple Criteria Decision Aid, in: J. Figueira, S. Greco, and M. Ehrgott, Eds, Multiple Criteria Decision Analysis: State of the Art Surveys, volume 78 of International Series in Operations Research and Management Science, chapter 12, pages 471-506. Springer Science + Business Media, Inc., New York.
Mobius.capacity-class
,
mini.var.capa.ident
,
mini.dist.capa.ident
,
ls.sorting.capa.ident
,
least.squares.capa.ident
,
heuristic.ls.capa.ident
,
entropy.capa.ident
.
## generate a random problem with 10 prototypes and 4 criteria n.proto <- 10 ## prototypes n <- 4 ## criteria P <- matrix(runif(n.proto*n,0,1),n.proto,n) ## the corresponding global scores, based on a randomly generated ## capacity a glob.eval <- numeric(n.proto) a <- capacity(c(0:(2^n-3),(2^n-3),(2^n-3))/(2^n-3)) for (i in 1:n.proto) glob.eval[i] <- Choquet.integral(a,P[i,]) ## based on these global scores, let us create a classification (3 classes) cl.proto<-numeric(n.proto) cl.proto[glob.eval <= 0.33] <- 1 cl.proto[glob.eval > 0.33 & glob.eval<=0.66] <-2 cl.proto[glob.eval > 0.66] <- 3 ## search for a capacity which satisfies the constraints lsc <- ls.sorting.capa.ident(n ,4, P, cl.proto, 0.1) ## output of the QP lsc$how ## analyse the quality of the model (classify the prototypes by the ## model and compare both assignments) lst <- ls.sorting.treatment(P,cl.proto,lsc$solution,P,cl.proto) ## assignments of the prototypes lst$class.A ## assignment types lst$correct.A ## evaluation lst$eval.correct ## generate a second set of random alternatives (A) ## their "correct" class is determined as beforehand with the ## randomly generated capacity a ## the goal is to see if we can reproduce this classification ## by the capacity learnt from the prototypes ## a randomly generated criteria matrix of 10 alternatives A <- matrix(runif(10*n,0,1),10,n) cl.orig.A <-numeric(10) ## the corresponding global scores glob.eval.A <- numeric(10) for (i in 1:10) glob.eval.A[i] <- Choquet.integral(a,A[i,]) ## based on these global scores, let us determine a classification cl.orig.A[glob.eval.A <= 0.33] <- 1 cl.orig.A[glob.eval.A>0.33 & glob.eval.A<=0.66] <-2 cl.orig.A[glob.eval.A > 0.66] <- 3 ## let us now classify the alternatives of A according to the model ## built on P lst <- ls.sorting.treatment(P,cl.proto,lsc$solution,A,cl.orig.A) ## assignment of the alternatives of A lst$class.A ## type of assignments lst$correct.A ## evaluation lst$eval.correct ## show the learnt capacity ## x11() ## barplot(Shapley.value(lsc$solution), main="Learnt capacity", sub="Shapley") ## summary of the learnt capacity lsc$solution summary(lsc$solution)