entropy.capa.ident {kappalab} | R Documentation |
This function estimates a capacity using as argument a set of data under the form: datum=(score on attribute 1, ..., score on attribute n). The approach roughly consists in replacing the subjective notion of importance of a subset of attributes by that of information content of a subset of attributes, which is estimated from the data by means of a parametric entropy measure. For more details, see the references hereafter.
entropy.capa.ident(d, entropy = "renyi", parameter = 1)
d |
An object of class |
entropy |
An object of class |
parameter |
An object of class |
Returns an object of class capacity
.
I. Kojadinovic (2004), Estimation of the weights of interacting criteria from the set of profiles by means of information-theoretic functionals, European Journal of Operational Research 155:3, pages 741-751.
I. Kojadinovic (2005), Unusupervised aggregation of commensurate correlated attributes by means of the Choquet integral and entropy functionals, International Journal of Intelligent Systems, in press.
capacity-class
,
lin.prog.capa.ident
,
mini.var.capa.ident
,
mini.dist.capa.ident
,
least.squares.capa.ident
,
heuristic.ls.capa.ident
,
ls.sorting.capa.ident
,
ls.ranking.capa.ident
.
## a set of randomly generated data ## for instance, marks on a [0,20] scale p <- data.frame(matrix(runif(500,0,20),100,5)) names(p) <- c("Stat","Prob","Alg","Cal","Eng") ## discretization p[p <= 5] <- 1 p[p > 5 & p <= 10] <- 2 p[p > 10 & p <= 15] <- 3 p[p > 15] <- 4 d <- data.frame(factor(p[[1]]), factor(p[[2]]), factor(p[[3]]), factor(p[[4]]), factor(p[[5]])) ## associated unsupervised capacity mu <- entropy.capa.ident(d) mu