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

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

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])