ownn: Optimal Weighted Nearest Neighbor Classification

In FNN: Fast Nearest Neighbor Search Algorithms and Applications

Optimal Weighted Nearest Neighbor Classification

Description

This function implements Samworth's optimal weighting scheme for k nearest neighbor classification. The performance improvement is greatest when the dimension is 4 as reported in the reference.

Usage

  ownn(train, test, cl, testcl=NULL, k = NULL, prob = FALSE,
      algorithm=c("kd_tree", "cover_tree", "brute"))

Arguments

train	matrix or data frame of training set cases.
test	matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case.
cl	factor of true classifications of training set.
testcl	factor of true classifications of testing set for error rate calculation.
k	number of neighbours considered, chosen by 5-fold cross-validation if not supplied.
prob	if this is true, the proportion of the weights for the winning class are returned as attribute prob.
algorithm	nearest neighbor search algorithm.

Value

a list includes k, predictions by ordinary knn, optimal weighted knn and bagged knn, and accuracies if class labels of test data set are given.

Author(s)

Shengqiao Li. To report any bugs or suggestions please email: [e-mail]

References

Richard J. Samworth (2012), “Optimal Weighted Nearest Neighbor Classifiers,” Annals of Statistics, 40:5, 2733-2763.

See Also

knn and knn in class.

Examples

    data(iris3)
    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)))
    testcl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
    out <- ownn(train, test, cl, testcl)
    out

FNN documentation built on Sept. 22, 2024, 9:06 a.m.