EkNNfit: Training of the EkNN classifier

Description

EkNNfit optimizes the parameters of the EkNN classifier.

Usage

EkNNfit(
  x,
  y,
  K,
  param = NULL,
  alpha = 0.95,
  lambda = 1/max(as.numeric(y)),
  optimize = TRUE,
  options = list(maxiter = 300, eta = 0.1, gain_min = 1e-06, disp = TRUE)
)

Value

A list with five elements:

param: The optimized parameters.
cost: Final value of the cost function.
err: Leave-one-out error rate.
ypred: Leave-one-out predicted class labels (coded as integers from 1 to M).
m: Leave-one-out predicted mass functions. The first M columns correspond to the mass assigned to each class. The last column corresponds to the mass assigned to the whole set of classes.

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).
K: Number of neighbors.
param: Initial parameters (default: NULL).
alpha: Parameter \(\alpha\) (default: 0.95)
lambda: Parameter of the cost function. If lambda=1, the cost function measures the error between the plausibilities and the 0-1 target values. If lambda=1/M, where M is the number of classes (default), the piginistic probabilities are considered in the cost function. If lambda=0, the beliefs are used.
optimize: Boolean. If TRUE (default), the parameters are optimized.
options: A list of parameters for the optimization algorithm: maxiter (maximum number of iterations), eta (initial step of gradient variation), gain_min (minimum gain in the optimisation loop), disp (Boolean; if TRUE, intermediate results are displayed during the optimization).

Author

Thierry Denoeux.

Details

If the argument param is not supplied, the function EkNNinit is called.

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.

Examples

Run this code

## Iris dataset
data(iris)
x<-iris[,1:4]
y<-iris[,5]
fit<-EkNNfit(x,y,K=5)

Run the code above in your browser using DataLab