evclass: Evidential Distance-Based Classification

Package: evclass 2.0.2

Thierry Denoeux

evclass: Evidential Distance-Based Classification

Different evidential classifiers, which provide outputs in the form of Dempster-Shafer mass functions. The methods are: the evidential K-nearest neighbor rule, the evidential neural network, radial basis function neural networks, logistic regression, feed-forward neural networks.

Authors:Thierry Denoeux

evclass_2.0.2.tar.gz
evclass_2.0.2.tar.gz(r-4.7-any)evclass_2.0.2.tar.gz(r-4.6-any)
evclass_2.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
evclass/json (API)

# Install 'evclass' in R:

install.packages('evclass', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

glass - Glass dataset
ionosphere - Ionosphere dataset
vehicles - Vehicles dataset

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.43 score 1 stars 27 scripts 345 downloads 1 mentions 12 exports 5 dependencies

Last updated from:636c59c083. Checks:4 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	108
source / vignettes	OK	172
linux-release-x86_64	OK	118
wasm-release	OK	109

Exports:calcAB calcm decision EkNNfit EkNNinit EkNNval proDSfit proDSinit proDSval RBFfit RBFinit RBFval

Dependencies:FNN ibelief R.methodsS3 R.oo R.utils

Introduction to the evclass package

Rendered fromIntroduction.Rmdusingknitr::rmarkdown

Help page	Topics
Determination of optimal coefficients for computing weights of evidence in logistic regression	calcAB
Determination of optimal coefficients for computing weights of evidence in logistic regression	calcm
Decision rules for evidential classifiers	decision
Training of the EkNN classifier	EkNNfit
Initialization of parameters for the EkNN classifier	EkNNinit
Classification of a test set by the EkNN classifier	EkNNval
evclass: A package for evidential classification	evclass
Glass dataset	glass
Ionosphere dataset	ionosphere
Training of the evidential neural network classifier	proDSfit
Initialization of parameters for the evidential neural network classifier	proDSinit
Classification of a test set by the evidential neural network classifier	proDSval
Training of a radial basis function classifier	RBFfit
Initialization of parameters for a Radial Basis Function classifier	RBFinit
Classification of a test set by a radial basis function classifier	RBFval
Vehicles dataset	vehicles

Help page

Topics

Determination of optimal coefficients for computing weights of evidence in logistic regression

calcAB

Determination of optimal coefficients for computing weights of evidence in logistic regression

calcm

Decision rules for evidential classifiers

decision

Training of the EkNN classifier

EkNNfit

Initialization of parameters for the EkNN classifier

EkNNinit