Multiedit for k-NN Classifier
Usage
multiedit(x, class, k=1, V=3, I=5, trace=T)
Arguments
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.
|
Description
Multiedit for k-NN classifierValue
index vector of cases to be retained.References
P. A. Devijver and J. Kittler (1982)
Pattern Recognition. A Statistical Approach.
Prentice-Hall, p. 115.See Also
condense
, reduce.nn
Examples
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))