multiedit {class} | R Documentation |
Multiedit for k-NN classifier
multiedit(x, class, k=1, V=3, I=5, trace=T)
x |
matrix of training set. |
class |
vector of classification of training set. |
k |
number of neighbours used in k-NN. |
V |
divide training set into V parts. |
I |
number of null passes before quitting. |
trace |
logical for statistics at each pass. |
index vector of cases to be retained.
P. A. Devijver and J. Kittler (1982) Pattern Recognition. A Statistical Approach. Prentice-Hall, p. 115.
data(iris3) tr <- sample(1:50,25) train <- rbind(iris3[tr,,1],iris3[tr,,2],iris3[tr,,3]) test <- rbind(iris3[-tr,,1],iris3[-tr,,2],iris3[-tr,,3]) cl <- factor(c(rep(1,25),rep(2,25), rep(3,25)), labels=c("s", "c", "v")) table(cl, knn(train, test, cl, 3)) ind1 <- multiedit(train, cl, 3) length(ind1) table(cl, knn(train[ind1,], test, cl[ind1], 1)) ntrain <- train[ind1,]; ncl <- cl[ind1] ind2 <- condense(ntrain, ncl) length(ind2) table(cl, knn(ntrain[ind2,], test, ncl[ind2], 1))