evclass {evclass} | R Documentation |
evclass: A package for evidential classification
Description
The evclass package currently contains functions for three evidential classifiers: the evidential K-nearest neighbor (EK-NN) rule (Denoeux, 1995; Zouhal and Denoeux, 1998), the evidential neural network (Denoeux, 2000) and the RBF classifier with weight-of-evidence interpretation (Denoeux, 2019; Huang et al., 2022), as well as methods to compute output mass functions from trained logistic regression or multilayer classifiers as described in (Denoeux, 2019). In contrast with classical statistical classifiers, evidential classifiers quantify the uncertainty of the classification using Dempster-Shafer mass functions.
Details
The main functions are: EkNNinit
, EkNNfit
and EkNNval
for the initialization, training and evaluation of the EK-NN classifier;
proDSinit
, proDSfit
and proDSval
for the
evidential neural network classifier; decision
for decision-making;
RBFinit
, RBFfit
and RBFval
for the RBF classifier;
calcAB
and calcm
for computing output mass functions from trained
logistic regression or multilayer classifiers.
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.
T. Denoeux. Analysis of evidence-theoretic decision rules for pattern classification. Pattern Recognition, 30(7):1095–1107, 1997.
T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Trans. on Systems, Man and Cybernetics A, 30(2):131–150, 2000.
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.
T. Denoeux. Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective. Knowledge-Based Systems, Vol. 176, Pages 54–67, 2019.
L., S. Ruan, P. Decazes and T. Denoeux. Lymphoma segmentation from 3D PET-CT images using a deep evidential network. International Journal of Approximate Reasoning, Vol. 149, Pages 39-60, 2022.
See Also
EkNNinit
, EkNNfit
,
EkNNval
, proDSinit
, proDSfit
, proDSval
,
RBFinit
, RBFfit
and RBFval
, decision
,
calcAB
, calcm
.