Help for package SKNN

Type:

Package

Title:

A Super K-Nearest Neighbor (SKNN) Classification Algorithm

Version:

4.1

Date:

2024-10-09

Maintainer:

Yarong Yang <Yi.YA_yaya@hotmail.com>

Depends:

methods, stats

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.

Collate:

SKNN.R Dist.R PCAy.R AllClasses.R KNN.R

License:

GPL-2

NeedsCompilation:

Packaged:

2024-10-08 18:35:48 UTC; yi_ya

Author:

Yarong Yang [aut, cre], Nader Ebrahimi [aut], Yoram Rubin [aut], Jacob Zhang [aut]

Repository:

CRAN

Date/Publication:

2024-10-08 20:10:01 UTC

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:	4.1
Date:	2024-10-09
License:	GPL-2

Author(s)

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

References

Yarong Yang, Nader Ebrahimi, and Yoram Rubin.(2024) SKNN: A Super K-Nearest Neighbor Classification Algorithm.

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))

Finding the distance between two observations.

Description

It's a function of finding the distance between two observations.

Usage

Dist(x,y)

Arguments

x

Numeric. A vector denoting an observation.

y

Numeric. A vector denoting an observation.

Value

A list.

Author(s)

Yarong Yang

K-Nearest Neighbor Classification

Description

It's implementation of the K-Nearest Neighbor classification method for data of any number of dimentions.

Usage

KNN(data, Class, k, test)

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.

Value

A character.

Author(s)

Yarong Yang

References

A.T. Covert and P. Hart. Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1): 21-27, 1967.

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]<-KNN(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("KNN Misclassified:",length(ind)))
axis(2,at=2,labels="KNN",las=1)
text(10,2.5,paste("k: ",k))

Revised PCA analysis

Description

It's a revised PCA analysis.

Usage

PCAy(data)

Arguments

data

Numeric. Data matrix for revised PCA analysis.

Value

An object of class "PCAyd".

Author(s)

Yarong Yang and Yoram Rubin

References

Yarong Yang, Nader Ebrahimi, and Yoram Rubin.(2024) SKNN: A Super K-Nearest Neighbor Classification Algorithm.

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)
dat<-cbind(Sepal.Length,Sepal.Width,Petal.Length,Petal.Width)
Res<-PCAy(dat)

Class to contain the results from revised PCA analysis.

Description

The function PCAy returns object of class PCAyd.

Objects from the Class

new("PCAyd",Var=new("numeric"),PC=new("matrix"),Scores=new("matrix"),IScores=new("numeric"))

Slots

Var:: An numeric vector giving the variance of each PC.
PC:: A numeric matrix about the coefficients of each PC.
Scores:: A numeric matrix showing the loading coefficiens of each PC.
IScores:: A numeric vector with each element being the rowsum of Scores.

Author(s)

Yarong Yang and Yoram Rubin

References

Yarong Yang, Nader Ebrahimi, and Yoram Rubin.(2024) SKNN: A Super K-Nearest Neighbor Classification Algorithm.

Examples

showClass("PCAyd")

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)

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.

Value

A character.

Author(s)

Yarong Yang, Nader Ebrahimi, and Yoram Rubin

References

Yarong Yang, Nader Ebrahimi, and Yoram Rubin.(2024) SKNN: A Super K-Nearest Neighbor Classification Algorithm.

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,])
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))