R: Reduce Training Set for a k-NN Classifier

reduce.nn {class} | R Documentation

## Reduce Training Set for a k-NN Classifier

### Description

Reduce training set for a k-NN classifier. Used after `condense`.

### Usage

    reduce.nn(train, ind, class)

### Arguments

`train` | matrix for training set
`ind` | Initial list of members of the training set (from `condense`).
`class` | vector of classifications for test set

### Details

All the members of the training set are tried in random order. Any which when dropped do not cause any members of the training set to be wrongly classified are dropped.

### Value

Index vector of cases to be retained.

### References

Gates, G.W. (1972) The reduced nearest neighbor rule. IEEE Trans. Information Theory IT-18, 431–432.

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

\link{condense}

, `multiedit`

### Examples

    train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
    test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
    cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
    keep <- condense(train, cl)
    knn(train[keep,], test, cl[keep])
    keep2 <- reduce.nn(train, keep, cl)
    knn(train[keep2,], test, cl[keep2])

[Package class version 7.3-3 Index]