evclass (version 2.0.2)

RBFval: Classification of a test set by a radial basis function classifier

Description

RBFval classifies instances in a test set using a radial basis function classifier. Function calcm is called for computing output belief functions. It is recommended to set calc.belief=FALSE when the number of classes is very large, to avoid memory problems.

Usage

RBFval(x, param, y = NULL, calc.belief = TRUE)

Value

A list with four elements:

ypred

Predicted class labels for the test data.

err

Test error rate (if the class label of test data has been provided).

Prob

Output probabilities.

Belief

If calc.belief=TRUE, output belief function, provided as a list output by function calcm.

Arguments

x

Matrix of size n x d, containing the values of the d attributes for the test data.

param

Neural network parameters, as provided by RBFfit.

y

Optional vector of class labels for the test data. May be a factor, or a vector of integers from 1 to M (number of classes).

calc.belief

If TRUE (default), output belief functions are calculated.

Author

Thierry Denoeux.

Details

If class labels for the test set are provided, the test error rate is also returned.

References

T. Denoeux. Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems, Vol. 176, Pages 54–67, 2019.

Ling Huang, Su Ruan, Pierre Decazes and Thierry Denoeux. Lymphoma segmentation from 3D PET-CT images using a deep evidential network. International Journal of Approximate Reasoning, Vol. 149, Pages 39-60, 2022.

See Also

RBFinit, RBFfit, calcm

Examples

## Glass dataset
data(glass)
xapp<-glass$x[1:89,]
yapp<-glass$y[1:89]
xtst<-glass$x[90:185,]
ytst<-glass$y[90:185]
## Initialization
param0<-RBFinit(xapp,yapp,nproto=7)
## Training
fit<-RBFfit(xapp,yapp,param0)
## Test
val<-RBFval(xtst,fit$param,ytst)
## Confusion matrix
table(ytst,val$ypred)