Package: noisemodel 1.0.2

José A. Sáez

noisemodel: Noise Models for Classification Datasets

Implementation of models for the controlled introduction of errors in classification datasets. This package contains the noise models described in Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, attributes and both simultaneously.

Authors:José A. Sáez [aut, cre]

noisemodel_1.0.2.tar.gz
noisemodel_1.0.2.tar.gz(r-4.5-noble)noisemodel_1.0.2.tar.gz(r-4.4-noble)
noisemodel_1.0.2.tgz(r-4.4-emscripten)noisemodel_1.0.2.tgz(r-4.3-emscripten)
noisemodel.pdf |noisemodel.html✨
noisemodel/json (API)
NEWS

# Install 'noisemodel' in R:

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

Datasets:

diris2D - Diris2D dataset
iris2D - Iris2D dataset

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

2.00 score 5 scripts 176 downloads 79 exports 90 dependencies

Last updated 2 years agofrom:554d3f013b. Checks:OK: 1 NOTE: 1. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 04 2024
R-4.5-linux	NOTE	Nov 04 2024

Exports:asy_def_ln asy_int_an asy_spa_ln asy_uni_an asy_uni_ln attm_uni_ln bord_dist bord_noise boud_gau_an clu_vot_ln exp_bor_ln exps_cuni_ln findnoise fra_bdir_ln gam_bor_ln gau_bor_ln gaum_bor_ln glev_uni_ln hubp_uni_ln imp_int_an irs_bdir_ln lap_bor_ln larm_uni_ln maj_udir_ln mind_bdir_ln minp_uni_ln mis_pre_ln mulc_udir_ln nei_bor_ln nlin_bor_ln noisetype oned_uni_ln opes_idnn_ln opes_idu_ln pai_bdir_ln pmd_con_ln qua_uni_ln runif_replace safe_sample sample_replace sco_con_ln sigb_uni_ln smam_bor_ln smu_cuni_ln sym_adj_ln sym_cen_ln sym_con_ln sym_cuni_an sym_cuni_cn sym_cuni_ln sym_ddef_ln sym_def_ln sym_dia_ln sym_dran_ln sym_end_an sym_exc_ln sym_gau_an sym_hie_ln sym_hienc_ln sym_int_an sym_natd_ln sym_nean_ln sym_nexc_ln sym_nuni_ln sym_opt_ln sym_pes_ln sym_sgau_an sym_uni_an sym_uni_ln sym_usim_ln symd_gau_an symd_gimg_an symd_rpix_an symd_uni_an ugau_bor_ln ulap_bor_ln unc_fixw_an unc_vgau_an uncs_guni_cn

Dependencies:C50 caret class classInt cli clock codetools colorspace cpp11 Cubist data.table diagram digest dplyr e1071 ExtDist fansi farver FNN foreach Formula future future.apply generics ggplot2 globals glue gower gtable hardhat inum ipred isoband iterators KernSmooth labeling lattice lava libcoin lifecycle listenv lsr lubridate magrittr MASS Matrix mgcv ModelMetrics munsell mvtnorm nlme nloptr nnet numDeriv optimx parallelly partykit pillar pkgconfig plyr pracma pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp recipes reshape2 rlang rpart RSNNS scales shape SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

Introduction to the noisemodel package

Rendered fromnoisemodel.Rmdusingknitr::rmarkdownon Nov 04 2024.

Last update: 2022-10-17
Started: 2022-10-17

Citation

Readme and manuals

Help Manual

Help page	Topics
Asymmetric default label noise	asy_def_ln asy_def_ln.default asy_def_ln.formula
Asymmetric interval-based attribute noise	asy_int_an asy_int_an.default asy_int_an.formula
Asymmetric sparse label noise	asy_spa_ln asy_spa_ln.default asy_spa_ln.formula
Asymmetric uniform attribute noise	asy_uni_an asy_uni_an.default asy_uni_an.formula
Asymmetric uniform label noise	asy_uni_ln asy_uni_ln.default asy_uni_ln.formula
Attribute-mean uniform label noise	attm_uni_ln attm_uni_ln.default attm_uni_ln.formula
Boundary/dependent Gaussian attribute noise	boud_gau_an boud_gau_an.default boud_gau_an.formula
Clustering-based voting label noise	clu_vot_ln clu_vot_ln.default clu_vot_ln.formula
diris2D dataset	diris2D
Exponential borderline label noise	exp_bor_ln exp_bor_ln.default exp_bor_ln.formula
Exponential/smudge completely-uniform label noise	exps_cuni_ln exps_cuni_ln.default exps_cuni_ln.formula
Fraud bidirectional label noise	fra_bdir_ln fra_bdir_ln.default fra_bdir_ln.formula
Gamma borderline label noise	gam_bor_ln gam_bor_ln.default gam_bor_ln.formula
Gaussian borderline label noise	gau_bor_ln gau_bor_ln.default gau_bor_ln.formula
Gaussian-mixture borderline label noise	gaum_bor_ln gaum_bor_ln.default gaum_bor_ln.formula
Gaussian-level uniform label noise	glev_uni_ln glev_uni_ln.default glev_uni_ln.formula
Hubness-proportional uniform label noise	hubp_uni_ln hubp_uni_ln.default hubp_uni_ln.formula
Importance interval-based attribute noise	imp_int_an imp_int_an.default imp_int_an.formula
iris2D dataset	iris2D
IR-stable bidirectional label noise	irs_bdir_ln irs_bdir_ln.default irs_bdir_ln.formula
Laplace borderline label noise	lap_bor_ln lap_bor_ln.default lap_bor_ln.formula
Large-margin uniform label noise	larm_uni_ln larm_uni_ln.default larm_uni_ln.formula
Majority-class unidirectional label noise	maj_udir_ln maj_udir_ln.default maj_udir_ln.formula
Minority-driven bidirectional label noise	mind_bdir_ln mind_bdir_ln.default mind_bdir_ln.formula
Minority-proportional uniform label noise	minp_uni_ln minp_uni_ln.default minp_uni_ln.formula
Misclassification prediction label noise	mis_pre_ln mis_pre_ln.default mis_pre_ln.formula
Multiple-class unidirectional label noise	mulc_udir_ln mulc_udir_ln.default mulc_udir_ln.formula
Neighborwise borderline label noise	nei_bor_ln nei_bor_ln.default nei_bor_ln.formula
Non-linearwise borderline label noise	nlin_bor_ln nlin_bor_ln.default nlin_bor_ln.formula
One-dimensional uniform label noise	oned_uni_ln oned_uni_ln.default oned_uni_ln.formula
Open-set ID/nearest-neighbor label noise	opes_idnn_ln opes_idnn_ln.default opes_idnn_ln.formula
Open-set ID/uniform label noise	opes_idu_ln opes_idu_ln.default opes_idu_ln.formula
Pairwise bidirectional label noise	pai_bdir_ln pai_bdir_ln.default pai_bdir_ln.formula
Plot function for class ndmodel	plot.ndmodel
PMD-based confidence label noise	pmd_con_ln pmd_con_ln.default pmd_con_ln.formula
Print function for class ndmodel	print.ndmodel
Quadrant-based uniform label noise	qua_uni_ln qua_uni_ln.default qua_uni_ln.formula
Score-based confidence label noise	sco_con_ln sco_con_ln.default sco_con_ln.formula
Sigmoid-bounded uniform label noise	sigb_uni_ln sigb_uni_ln.default sigb_uni_ln.formula
Small-margin borderline label noise	smam_bor_ln smam_bor_ln.default smam_bor_ln.formula
Smudge-based completely-uniform label noise	smu_cuni_ln smu_cuni_ln.default smu_cuni_ln.formula
Summary function for class ndmodel	summary.ndmodel
Symmetric adjacent label noise	sym_adj_ln sym_adj_ln.default sym_adj_ln.formula
Symmetric center-based label noise	sym_cen_ln sym_cen_ln.default sym_cen_ln.formula
Symmetric confusion label noise	sym_con_ln sym_con_ln.default sym_con_ln.formula
Symmetric completely-uniform attribute noise	sym_cuni_an sym_cuni_an.default sym_cuni_an.formula
Symmetric completely-uniform combined noise	sym_cuni_cn sym_cuni_cn.default sym_cuni_cn.formula
Symmetric completely-uniform label noise	sym_cuni_ln sym_cuni_ln.default sym_cuni_ln.formula
Symmetric double-default label noise	sym_ddef_ln sym_ddef_ln.default sym_ddef_ln.formula
Symmetric default label noise	sym_def_ln sym_def_ln.default sym_def_ln.formula
Symmetric diametrical label noise	sym_dia_ln sym_dia_ln.default sym_dia_ln.formula
Symmetric double-random label noise	sym_dran_ln sym_dran_ln.default sym_dran_ln.formula
Symmetric end-directed attribute noise	sym_end_an sym_end_an.default sym_end_an.formula
Symmetric exchange label noise	sym_exc_ln sym_exc_ln.default sym_exc_ln.formula
Symmetric Gaussian attribute noise	sym_gau_an sym_gau_an.default sym_gau_an.formula
Symmetric hierarchical label noise	sym_hie_ln sym_hie_ln.default sym_hie_ln.formula
Symmetric hierarchical/next-class label noise	sym_hienc_ln sym_hienc_ln.default sym_hienc_ln.formula
Symmetric interval-based attribute noise	sym_int_an sym_int_an.default sym_int_an.formula
Symmetric natural-distribution label noise	sym_natd_ln sym_natd_ln.default sym_natd_ln.formula
Symmetric nearest-neighbor label noise	sym_nean_ln sym_nean_ln.default sym_nean_ln.formula
Symmetric next-class label noise	sym_nexc_ln sym_nexc_ln.default sym_nexc_ln.formula
Symmetric non-uniform label noise	sym_nuni_ln sym_nuni_ln.default sym_nuni_ln.formula
Symmetric optimistic label noise	sym_opt_ln sym_opt_ln.default sym_opt_ln.formula
Symmetric pessimistic label noise	sym_pes_ln sym_pes_ln.default sym_pes_ln.formula
Symmetric scaled-Gaussian attribute noise	sym_sgau_an sym_sgau_an.default sym_sgau_an.formula
Symmetric uniform attribute noise	sym_uni_an sym_uni_an.default sym_uni_an.formula
Symmetric uniform label noise	sym_uni_ln sym_uni_ln.default sym_uni_ln.formula
Symmetric unit-simplex label noise	sym_usim_ln sym_usim_ln.default sym_usim_ln.formula
Symmetric/dependent Gaussian attribute noise	symd_gau_an symd_gau_an.default symd_gau_an.formula
Symmetric/dependent Gaussian-image attribute noise	symd_gimg_an symd_gimg_an.default symd_gimg_an.formula
Symmetric/dependent random-pixel attribute noise	symd_rpix_an symd_rpix_an.default symd_rpix_an.formula
Symmetric/dependent uniform attribute noise	symd_uni_an symd_uni_an.default symd_uni_an.formula
Uneven-Gaussian borderline label noise	ugau_bor_ln ugau_bor_ln.default ugau_bor_ln.formula
Uneven-Laplace borderline noise	ulap_bor_ln ulap_bor_ln.default ulap_bor_ln.formula
Unconditional fixed-width attribute noise	unc_fixw_an unc_fixw_an.default unc_fixw_an.formula
Unconditional vp-Gaussian attribute noise	unc_vgau_an unc_vgau_an.default unc_vgau_an.formula
Unconditional/symmetric Gaussian/uniform combined noise	uncs_guni_cn uncs_guni_cn.default uncs_guni_cn.formula