ClassificationError {DRquality}

Classification Error (rate)

Description

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

Usage

ClassificationError(OutputDistances,Cls,k=5)

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)

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].

Value

List with three entries:

Error

Classification Error: 1-Accuracy[1]

Accuracy

Accuracy

KNNCls

[1:n]] cls of knn classifier

Note

Here, the Outputdistances of the Projected points are used.

Author(s)

Michael Thrun

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

if(requireNamespace("FCPS")){
data(Hepta,package="FCPS")
projection=cmdscale(dist(Hepta$Data), k=2)
ClassificationError(as.matrix(dist(projection)),Hepta$Cls)
}