proDSinit: Initialization of parameters for the evidential neural...

In evclass: Evidential Distance-Based Classification

Initialization of parameters for the evidential neural network classifier

Description

proDSinit returns initial parameter values for the evidential neural network classifier.

Usage

proDSinit(x, y, nproto, nprotoPerClass = FALSE, crisp = FALSE)

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 labels (of length n). May be a factor, or a vector of integers from 1 to M (number of classes).
nproto	Number of prototypes.
nprotoPerClass	Boolean. If TRUE, there are nproto prototypes per class. If FALSE (default), the total number of prototypes is equal to nproto.
crisp	Boolean. If TRUE, the prototypes have full membership to only one class. (Available only if nprotoPerClass=TRUE).

Details

The prototypes are initialized by the k-means algorithms. The initial membership values u_{ik} of each prototype p_i to class \omega_k are normally defined as the proportion of training samples from class \omega_k in the neighborhood of prototype p_i. If arguments crisp and nprotoPerClass are set to TRUE, the prototypes are assigned to one and only one class.

Value

A list with four elements containing the initialized network parameters

alpha

Vector of length r, where r is the number of prototypes.

gamma

Vector of length r

beta

Matrix of size (r,M), where M is the number of classes.

W

Matrix of size (r,d), containing the prototype coordinates.

Author(s)

Thierry Denoeux.

References

T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Trans. on Systems, Man and Cybernetics A, 30(2):131–150, 2000.

See Also

proDSfit, proDSval

Examples

## Glass dataset
data(glass)
xapp<-glass$x[1:89,]
yapp<-glass$y[1:89]
param0<-proDSinit(xapp,yapp,nproto=7)
param0

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