Multiedit for k-NN Classifier

DESCRIPTION:

Multiedit for k-NN classifier

USAGE:

multiedit(x, class, k=1, V=3, I=5, trace=T)

REQUIRED ARGUMENTS:

x
matrix of training set.
class
vector of classification of training set.
k
number of neighbours used in k-NN.

OPTIONAL ARGUMENTS:

V
divide training set into V parts.
I
number of null passes before quitting.
trace
logical for statistics at each pass.

VALUE:

index vector of cases to be retained.

REFERENCE:

P. A. Devijver and J. Kittler (1982) Pattern Recognition. A Statistical Approach. Prentice-Hall, p. 115.

SEE ALSO:

,

EXAMPLES:

set.seed(99)
tr <- sample(1:50,25)
train <- rbind(iris[tr,,1],iris[tr,,2],iris[tr,,3])
test <- rbind(iris[-tr,,1],iris[-tr,,2],iris[-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, , drop=F], test, cl[ind1], 1))
ntrain <- train[ind1,]; ncl <- cl[ind1]
ind2 <- condense(ntrain, ncl)
length(ind2)
table(cl, knn(ntrain[ind2, , drop=F], test, ncl[ind2], 1))