Condense training set for k-NN classifier

Condense training set for k-NN classifier

condense(train, class, store, trace = TRUE)

Arguments

train

matrix for training set

class

vector of classifications for test set

store

initial store set. Default one randomly chosen element of the set.

trace

logical. Trace iterations?

Details

The store set is used to 1-NN classify the rest, and misclassified patterns are added to the store set. The whole set is checked until no additions occur.

Value

Index vector of cases to be retained (the final store set).

References

P. A. Devijver and J. Kittler (1982) Pattern Recognition. A Statistical Approach. Prentice-Hall, pp. 119–121.

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

reduce.nn, 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)
#> [1] 33
#> [1]  3 33
#> [1]  3 33 61
#> [1]  3 32 33 61
#> [1]  3 32 33 61 72
#> [1]  3 32 33 42 61 72
#> [1]  3 32 33 42 57 61 72
#> [1]  3 32 33 35 42 57 61 72
knn(train[keep, , drop=FALSE], test, cl[keep])
#>  [1] s s s s s s s s s s s s s s s s s s s s s s s s s c c v c c c c c v c c c c
#> [39] c c c c c c c c c c c c v c c v v v v v c v v v v c v v v v v v v v v v v
#> Levels: c s v
keep2 <- reduce.nn(train, keep, cl)
knn(train[keep2, , drop=FALSE], test, cl[keep2])
#>  [1] s s s s s s s s s s s s s s s s s s s s s s s s s c c v c c c c c v c c c c
#> [39] c c c c c c c c c c c c v c c v v v v v c v v v v c v v v v v v v v v v v
#> Levels: c s v