# Super K-Nearest Neighbor (SKNN) Classification It's a Super K-Nearest Neighbor (SKNN) classification method with using kernel density to describe the weight of the distance between a training observation and the sample to be classified. Comparison of performance between SKNN and KNN shows that SKNN is significantly superior to KNN. package ## Details ||| |:--|:--| |Package:|SKNN| |Type:|Package| |Version:|4.1.3| |Date:|2026-02-08| |License:|GPL-2| ## Author(s) 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.(1986) Density Estimation for Statistics and Data Analysis. London: Chapman and Hall. ## Examples ```r 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)) ```

SKNN-package