EkNNinit | R Documentation |
EkNNinit
returns initial parameter values for the EkNN classifier.
EkNNinit(x, y, alpha = 0.95)
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 |
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.
A list with two elements:
Vector of parameters \gamma_k
, of length c, the number of classes.
Parameter \alpha
, set to 0.95.
Thierry Denoeux.
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.
EkNNfit
, EkNNval
## Iris dataset
data(iris)
x<-iris[,1:4]
y<-iris[,5]
param<-EkNNinit(x,y)
param
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.