myownn: Optimal Weighted Nearest Neighbor Classifier

In snn: Stabilized 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)

snn documentation built on May 1, 2019, 7:05 p.m.