multiedit: Multiedit for k-NN Classifier

In class: Functions for Classification

Description

Multiedit for k-NN classifier

Usage

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

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.

Value

Index vector of cases to be retained.

References

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

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

condense, reduce.nn

Examples

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, , drop=FALSE], test, cl[ind1], 1))
ntrain <- train[ind1,]; ncl <- cl[ind1]
ind2 <- condense(ntrain, ncl)
length(ind2)
table(cl, knn(ntrain[ind2, , drop=FALSE], test, ncl[ind2], 1))

Example output

   
cl   s  c  v
  s 25  0  0
  c  0 23  2
  v  0  1 24
pass 1 size 70
pass 2 size 67
pass 3 size 67
pass 4 size 64
pass 5 size 62
pass 6 size 62
pass 7 size 62
pass 8 size 62
pass 9 size 62
[1] 62
   
cl   s  c  v
  s 25  0  0
  c  0 18  7
  v  0  1 24
[1] 43
[1] 36 43
[1] 16 36 43
[1] 16 31 36 43
[1] 16 31 36 43 62
[1] 16 31 36 43 52 62
[1] 6
   
cl   s  c  v
  s 25  0  0
  c  0 17  8
  v  0  0 25

class documentation built on Jan. 13, 2022, 9:07 a.m.