SKNN-package {SKNN}

Super K-Nearest Neighbor (SKNN) Classification

Description

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

Details

Package:	SKNN
Type:	Package
Version:	3.1
Date:	2022-06-11
License:	GPL-2

Author(s)

Yi Ya, Nader Ebrahimi, Yoram Rubin, and Jacob Zhang

References

Yarong Yang(Yi Ya).(2022) SKNN: A Super K-Nearest Neighbor Classification Algorithm. submitted to Journal of Statistical Software

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.(1986) Density Estimation for Statistics and Data Analysis. London: Chapman and Hall.

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,])
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))