myownn {snn} | R Documentation |
Optimal Weighted Nearest Neighbor Classifier
Description
Implement Samworth's optimal weighted nearest neighbor classification algorithm to predict the label of a new input using a training data set.
Usage
myownn(train, test, K)
Arguments
train |
Matrix of training data sets. An n by (d+1) matrix, where n is the sample size and d is the dimension. The last column is the class label. |
test |
Vector of a test point. It also admits a matrix input with each row representing a new test point. |
K |
Number of nearest neighbors considered. |
Details
The tuning parameter K can be tuned via cross-validation, see cv.tune function for the tuning procedure.
Value
It returns the predicted class label of the new test point. If input is a matrix, it returns a vector which contains the predicted class labels of all the new test points.
Author(s)
Wei Sun, Xingye Qiao, and Guang Cheng
References
R.J. Samworth (2012), "Optimal Weighted Nearest Neighbor Classifiers," Annals of Statistics, 40:5, 2733-2763.
Examples
# Training data
set.seed(1)
n = 100
d = 10
DATA = mydata(n, d)
# Testing data
set.seed(2015)
ntest = 100
TEST = mydata(ntest, d)
TEST.x = TEST[,1:d]
# optimal weighted nearest neighbor classifier
myownn(DATA, TEST.x, K = 5)