SKNN (version 4.1.3)

SKNN: Super K-Nearest Neighbor (SKNN) Classification

Description

It's a Super K-Nearest Neighbor classification method with using kernel density to describe weight of the distance between a training observation and the testing sample.

Usage

SKNN(data, Class, k, test, ker)

Value

A character.

Arguments

data

Numeric. The training data set, matrix.

Class

Character. Class of the training observations, vector.

k

Integer. The number of K to be used.

test

Numeric. The sample to be classified.

ker

Character. 'GK', 'EK', or 'TK'. The kernel function to be used.

Author

Yarong Yang, Nader Ebrahimi, Yoram Rubin, and Jacob Zhang

References

Yarong Yang, Nader Ebrahimi, Yoram Rubin, and Jacob Zhang.(2025) SKNN: A Super K-Nearest Neighbor Classification Algorithm. technical report in preparation

Yarong Yang, Matt Over, and Yoram Rubin.(2012) Strategic Placement of Localization Devices (such as Pilot Points and Anchors) in Inverse Modeling Schemes. Water Resources Research, 48, W08519, doi:10.1029/2012WR011864.

B.B.W. Silverman. Density Estimation for Statistics and Data Analysis. London: Chapman and Hall, 1986.

Examples

Sepal.Length<-c(4.8, 5.1, 4.6, 5.3, 5.0, 5.7, 5.7, 6.2, 5.1, 5.7, 6.7, 6.3, 6.5, 6.2, 5.9)
Sepal.Width<-c(3.0, 3.8, 3.2, 3.7, 3.3, 3.0, 2.9, 2.9, 2.5, 2.8, 3.0, 2.5, 3.0, 3.4, 3.0)
Petal.Length<-c(1.4, 1.6, 1.4, 1.5, 1.4, 4.2, 4.2, 4.3, 3.0, 4.1, 5.2, 5.0, 5.2, 5.4, 5.1)
Petal.Width<-c(0.3, 0.2, 0.2, 0.2, 0.2, 1.2, 1.3, 1.3, 1.1, 1.3, 2.3, 1.9, 2.0, 2.3, 1.8)
Species<-as.factor(c(rep("red",5),rep("blue",5),rep("green",5)))
iris<-cbind(Sepal.Length,Sepal.Width,Petal.Length,Petal.Width)
Res<-length(nrow(iris))
k<-10
for(i in 1:nrow(iris)) 
     Res[i]<-SKNN(data=iris,Class=as.vector(Species),k=k,test=iris[i,],ker="GK")
accuracy<-length(which(Res==Species))/length(Species)
plot(x=1:15,y=rep(1,15),col=as.vector(Species),lwd=4,ylim=c(0,3),xlab="",ylab="",
yaxt = "n",xaxt="n")
par(new=TRUE)
plot(x=1:15,y=rep(2,15),col=Res,lwd=4,ylim=c(0,3),xlab="",ylab="",yaxt = "n",xaxt="n")
ind<-which(Res!=Species)
if(length(ind)>0) {
  for(j in 1:length(ind))
      lines(x=c(ind[j],ind[j]),y=c(1+0.05,2-0.05))
}
text(5,0.3,paste("SKNN Misclassified:",length(ind)))
axis(2,at=2,labels="SKNN",las=1)
text(10,2.5,paste("k: ",k))