Condense training set for k-NN classifier

DESCRIPTION:

Condense training set for k-NN classifier

USAGE:

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

REQUIRED ARGUMENTS:

train
matrix for training set
class
vector of classifications for test set

OPTIONAL ARGUMENTS:

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).

REFERENCE:

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

SEE ALSO:

,

EXAMPLES:

train <- rbind(iris[1:25,,1],iris[1:25,,2],iris[1:25,,3])
test <- rbind(iris[26:50,,1],iris[26:50,,2],iris[26:50,,3])
cl <- factor(c(rep("s",25),rep("c",25), rep("v",25)))
set.seed(99)
keep <- condense(train, cl)
knn(train[keep, , drop=F], test, cl[keep])
keep2 <- reduce.nn(train, keep, cl)
knn(train[keep2, , drop=F], test, cl[keep2])