mybnn {snn}

Bagged Nearest Neighbor Classifier

Description

Implement the bagged nearest neighbor classification algorithm to predict the label of a new input using a training data set.

Usage

mybnn(train, test, ratio)

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.

ratio

Resampling ratio.

Details

The bagged nearest neighbor classifier is asymptotically equivalent to a weighted nearest neighbor classifier with the i-th weight a function of the resampling ratio, the sample size n, and i. See Hall and Samworth (2005) for details. The tuning parameter ratio 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

Hall, P. and Samworth, R. (2005). Properties of Bagged Nearest Neighbor Classifiers. Journal of the Royal Statistical Society, Series B, 67, 363-379.

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]
	
	# bagged nearest neighbor classifier
	mybnn(DATA, TEST.x, ratio = 0.5)