EkNNinit {evclass}

Initialization of parameters for the EkNN classifier

Description

EkNNinit returns initial parameter values for the EkNN classifier.

Usage

EkNNinit(x, y, alpha = 0.95)

Arguments

x

Input matrix of size n x d, where n is the number of objects and d the number of attributes.

y

Vector of class lables (of length n). May be a factor, or a vector of integers from 1 to M (number of classes).

alpha

Parameter \alpha.

Details

Each parameter \gamma_k is set ot the inverse of the square root of the mean Euclidean distances wihin class k. Note that \gamma_k here is the square root of the \gamma_k as defined in (Zouhal and Denoeux, 1998). By default, parameter alpha is set to 0.95. This value normally does not have to be changed.

Value

A list with two elements:

gamma

Vector of parameters \gamma_k, of length c, the number of classes.

alpha

Parameter \alpha, set to 0.95.

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

EkNNfit, EkNNval

Examples

## Iris dataset
data(iris)
x<-iris[,1:4]
y<-iris[,5]
param<-EkNNinit(x,y)
param