EkNNval: Classification of a test set by the EkNN classifier

In evclass: Evidential Distance-Based Classification

Classification of a test set by the EkNN classifier

Description

EkNNval classifies instances in a test set using the EkNN classifier.

Usage

EkNNval(xtrain, ytrain, xtst, K, ytst = NULL, param = NULL)

Arguments

xtrain	Matrix of size ntrain x d, containing the values of the d attributes for the training data.
ytrain	Vector of class labels for the training data (of length ntrain). May be a factor, or a vector of integers from 1 to M (number of classes).
xtst	Matrix of size ntst x d, containing the values of the d attributes for the test data.
K	Number of neighbors.
ytst	Vector of class labels for the test data (optional). May be a factor, or a vector of integers from 1 to M (number of classes).
param	Parameters, as returned by EkNNfit.

Details

If class labels for the test set are provided, the test error rate is also returned. If parameters are not supplied, they are given default values by EkNNinit.

Value

A list with three elements:

m

Predicted mass functions for the test data. The first M columns correspond to the mass assigned to each class. The last column corresponds to the mass assigned to the whole set of classes.

ypred

Predicted class labels for the test data (coded as integers from 1 to M).

err

Test error rate.

Author(s)

Thierry Denoeux.

References

T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics, 25(05):804–813, 1995.

L. M. Zouhal and T. Denoeux. An evidence-theoretic k-NN rule with parameter optimization. IEEE Transactions on Systems, Man and Cybernetics Part C, 28(2):263–271,1998.

See Also

EkNNinit, EkNNfit

Examples

## Iris dataset
data(iris)
train<-sample(150,100)
xtrain<-iris[train,1:4]
ytrain<-iris[train,5]
xtst<-iris[-train,1:4]
ytst<-iris[-train,5]
K<-5
fit<-EkNNfit(xtrain,ytrain,K)
test<-EkNNval(xtrain,ytrain,xtst,K,ytst,fit$param)

evclass documentation built on Nov. 9, 2023, 5:08 p.m.