# multiedit: Multiedit for k-NN Classifier In class: Functions for Classification

## Description

Multiedit for k-NN classifier

## Usage

 `1` ```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.

`condense`, `reduce.nn`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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
 62

cl   s  c  v
s 25  0  0
c  0 18  7
v  0  1 24
 43
 36 43
 16 36 43
 16 31 36 43
 16 31 36 43 62
 16 31 36 43 52 62
 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.