abcrlda: Asymptotically Bias-Corrected Regularized Linear Discriminant Analysis

Offers methods to perform asymptotically bias-corrected regularized linear discriminant analysis (ABC_RLDA) for cost-sensitive binary classification. The bias-correction is an estimate of the bias term added to regularized discriminant analysis (RLDA) that minimizes the overall risk. The default magnitude of misclassification costs are equal and set to 0.5; however, the package also offers the options to set them to some predetermined values or, alternatively, take them as hyperparameters to tune. A. Zollanvari, M. Abdirash, A. Dadlani and B. Abibullaev (2019) <doi:10.1109/LSP.2019.2918485>.

Version: 1.0.2
Imports: stats
Published: 2020-01-29
Author: Dmitriy Fedorov [aut, cre], Amin Zollanvari [aut], Aresh Dadlani [aut], Berdakh Abibullaev [aut]
Maintainer: Dmitriy Fedorov <dmitriy.fedorov at nu.edu.kz>
License: GPL-3
URL: https://ieeexplore.ieee.org/document/8720003/, https://dx.doi.org/10.1109/LSP.2019.2918485
NeedsCompilation: no
CRAN checks: abcrlda results

Downloads:

Reference manual: abcrlda.pdf
Package source: abcrlda_1.0.2.tar.gz
Windows binaries: r-devel: abcrlda_1.0.2.zip, r-devel-gcc8: abcrlda_1.0.2.zip, r-release: abcrlda_1.0.2.zip, r-oldrel: abcrlda_1.0.2.zip
OS X binaries: r-release: abcrlda_1.0.2.tgz, r-oldrel: abcrlda_1.0.2.tgz
Old sources: abcrlda archive

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