EkNNinit function

Initialization of parameters for the EkNN classifier

EkNNinit returns initial parameter values for the EkNN classifier.

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 $\alpha$.

Returns

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.

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.

Examples

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

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

Author(s)

Thierry Denoeux.