kknn: Weighted k-Nearest Neighbor Classifier

In kknn: Weighted k-Nearest Neighbors

Weighted k-Nearest Neighbor Classifier

Description

Performs k-nearest neighbor classification of a test set using a training set. For each row of the test set, the k nearest training set vectors (according to Minkowski distance) are found, and the classification is done via the maximum of summed kernel densities. In addition even ordinal and continuous variables can be predicted.

Usage

kknn(
  formula = formula(train),
  train,
  test,
  na.action = na.omit(),
  k = 7,
  distance = 2,
  kernel = "optimal",
  ykernel = NULL,
  scale = TRUE,
  contrasts = c(unordered = "contr.dummy", ordered = "contr.ordinal")
)

kknn.dist(learn, valid, k = 10, distance = 2)

## S3 method for class 'kknn'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'kknn'
summary(object, ...)

## S3 method for class 'kknn'
predict(object, type = c("raw", "prob"), ...)

Arguments

formula	A formula object.
train	Matrix or data frame of training set cases.
test	Matrix or data frame of test set cases.
na.action	A function which indicates what should happen when the data contain 'NA's.
k	Number of neighbors considered.
distance	Parameter of Minkowski distance.
kernel	Kernel to use. Possible choices are "rectangular" (which is standard unweighted knn), "triangular", "epanechnikov" (or beta(2,2)), "biweight" (or beta(3,3)), "triweight" (or beta(4,4)), "cos", "inv", "gaussian", "rank" and "optimal".
ykernel	Window width of an y-kernel, especially for prediction of ordinal classes.
scale	logical, scale variable to have equal sd.
contrasts	A vector containing the 'unordered' and 'ordered' contrasts to use.
learn	Matrix or data frame of training set cases.
valid	Matrix or data frame of test set cases.
x	an object used to select a method.
digits	minimal number of significant digits.
...	further arguments passed to or from other methods.
object	a model object for which prediction is desired.
type	defines the output, 'raw' returns the estimates, 'prob' returns a matrix containing the proportions of each class.

Details

This nearest neighbor method expands knn in several directions. First it can be used not only for classification, but also for regression and ordinal classification. Second it uses kernel functions to weight the neighbors according to their distances. In fact, not only kernel functions but every monotonic decreasing function f(x) \forall x>0 will work fine.

The number of neighbours used for the "optimal" kernel should be [ (2(d+4)/(d+2))^(d/(d+4)) k ], where k is the number that would be used for unweighted knn classification, i.e. kernel="rectangular". This factor (2(d+4)/(d+2))^(d/(d+4)) is between 1.2 and 2 (see Samworth (2012) for more details).

Value

kknn returns a list-object of class kknn including the components

fitted.values	Vector of predictions.
CL	Matrix of classes of the k nearest neighbors.
W	Matrix of weights of the k nearest neighbors.
D	Matrix of distances of the k nearest neighbors.
C	Matrix of indices of the k nearest neighbors.
prob	Matrix of predicted class probabilities.
response	Type of response variable, one of continuous, nominal or ordinal.
distance	Parameter of Minkowski distance.
call	The matched call.
terms	The 'terms' object used.

Author(s)

Klaus P. Schliep [e-mail]
Klaus Hechenbichler

References

Hechenbichler K. and Schliep K.P. (2004) Weighted k-Nearest-Neighbor Techniques and Ordinal Classification, Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich (\Sexpr[results=rd]{tools:::Rd_expr_doi("10.5282/ubm/epub.1769")})

Hechenbichler K. (2005) Ensemble-Techniken und ordinale Klassifikation, PhD-thesis

Samworth, R.J. (2012) Optimal weighted nearest neighbour classifiers. Annals of Statistics, 40, 2733-2763. (available from http://www.statslab.cam.ac.uk/~rjs57/Research.html)

See Also

train.kknn

Examples

library(kknn)

data(iris)
m <- dim(iris)[1]
val <- sample(1:m, size = round(m/3), replace = FALSE, 
	prob = rep(1/m, m)) 
iris.learn <- iris[-val,]
iris.valid <- iris[val,]
iris.kknn <- kknn(Species~., iris.learn, iris.valid, distance = 1,
	kernel = "triangular")
summary(iris.kknn)
fit <- fitted(iris.kknn)
table(iris.valid$Species, fit)
pcol <- as.character(as.numeric(iris.valid$Species))
pairs(iris.valid[1:4], pch = pcol, col = c("green3", "red")
	[(iris.valid$Species != fit)+1])

data(ionosphere)
ionosphere.learn <- ionosphere[1:200,]
ionosphere.valid <- ionosphere[-c(1:200),]
fit.kknn <- kknn(class ~ ., ionosphere.learn, ionosphere.valid)
table(ionosphere.valid$class, fit.kknn$fit)
(fit.train1 <- train.kknn(class ~ ., ionosphere.learn, kmax = 15, 
	kernel = c("triangular", "rectangular", "epanechnikov", "optimal"), distance = 1))
table(predict(fit.train1, ionosphere.valid), ionosphere.valid$class)
(fit.train2 <- train.kknn(class ~ ., ionosphere.learn, kmax = 15, 
	kernel = c("triangular", "rectangular", "epanechnikov", "optimal"), distance = 2))
table(predict(fit.train2, ionosphere.valid), ionosphere.valid$class)

kknn documentation built on April 16, 2025, 9:07 a.m.