EkNNinit {evclass} | R Documentation |
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 |
Details
Each parameter is set ot the inverse of the square root of the mean
Euclidean distances wihin class k. Note that
here is the square root
of the
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
, of length c, the number of classes.
- alpha
Parameter
, 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
Examples
## Iris dataset
data(iris)
x<-iris[,1:4]
y<-iris[,5]
param<-EkNNinit(x,y)
param