DRquality (version 0.2.1)

ClassificationError: Classification Error (rate)

Description

Compares projected points to a given prior classification using knn classifier.

Usage

ClassificationError(OutputDistances,Cls,k=5)

Value

List with three entries:

Error

Classification Error: 1-Accuracy[1]

Accuracy

Accuracy

KNNCls

[1:n]] cls of knn classifier

Arguments

OutputDistances

[1:n,1:n] numeric matrix with distance matrix of projected data.

Cls

[1:n] Numeric vector containing class information.

k

number of k nearest neighbors, in Venna 2010 set to 5 (here default)

Author

Michael Thrun

Details

Projected points are evaluated by k-nearest neighbor classification accuracy (with k = 5), that is, each sample in the visualization is classified by majority vote of its k nearest neighbors in the visualization, and the classification is compared to the ground truth label. [Venna 2010].

References

Venna, J., Peltonen, J., Nybo, K., Aidos, H., and Kaski, S. Information retrieval perspective to nonlinear dimensionality reduction for data visualization. The Journal of Machine Learning Research, 11, 451-490. (2010)

Gracia, A., Gonzalez, S., Robles, V., and Menasalvas, E. A methodology to compare Dimensionality Reduction algorithms in terms of loss of quality. Information Sciences, 270, 1-27. (2014)

Examples

# \donttest{
if(requireNamespace("FCPS")){
data(Hepta,package="FCPS")
projection=cmdscale(dist(Hepta$Data), k=2)
ClassificationError(as.matrix(dist(projection)),Hepta$Cls)
}
# }
# \dontshow{
n=100
Data=cbind(rnorm(100),rnorm(100,2,3))
Cls=c(rep(1,50),rep(2,50))
projection=cmdscale(dist(Data), k=2)
ClassificationError(as.matrix(dist(projection)),Cls)
# }