ownn {FNN} R Documentation

## 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: lishengqiao@yahoo.com

### References

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

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


[Package FNN version 1.1.3.2 Index]