Installation | Documentation | Usage of noise models | usage of the default method | show results | usage of the method for class formula | check the match of noisy indices
Package: noisemodel 1.0.2
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:
noisemodel_1.0.2.tar.gz
noisemodel_1.0.2.zip(r-4.7)noisemodel_1.0.2.zip(r-4.6)noisemodel_1.0.2.zip(r-4.5)
noisemodel_1.0.2.tgz(r-4.6-any)noisemodel_1.0.2.tgz(r-4.5-any)
noisemodel_1.0.2.tar.gz(r-4.7-any)noisemodel_1.0.2.tar.gz(r-4.6-any)
noisemodel_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
noisemodel/json (API)
| # Install 'noisemodel' in R: |
| install.packages('noisemodel', repos = c('https://joseasaezm.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:554d3f013b. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | NOTE | 338 | ||
| source / vignettes | OK | 238 | ||
| linux-release-x86_64 | NOTE | 213 | ||
| macos-release-arm64 | NOTE | 248 | ||
| macos-oldrel-arm64 | NOTE | 211 | ||
| windows-devel | NOTE | 165 | ||
| windows-release | NOTE | 187 | ||
| windows-oldrel | NOTE | 174 | ||
| wasm-release | OK | 130 |
Exports:asy_def_lnasy_int_anasy_spa_lnasy_uni_anasy_uni_lnattm_uni_lnbord_distbord_noiseboud_gau_anclu_vot_lnexp_bor_lnexps_cuni_lnfindnoisefra_bdir_lngam_bor_lngau_bor_lngaum_bor_lnglev_uni_lnhubp_uni_lnimp_int_anirs_bdir_lnlap_bor_lnlarm_uni_lnmaj_udir_lnmind_bdir_lnminp_uni_lnmis_pre_lnmulc_udir_lnnei_bor_lnnlin_bor_lnnoisetypeoned_uni_lnopes_idnn_lnopes_idu_lnpai_bdir_lnpmd_con_lnqua_uni_lnrunif_replacesafe_samplesample_replacesco_con_lnsigb_uni_lnsmam_bor_lnsmu_cuni_lnsym_adj_lnsym_cen_lnsym_con_lnsym_cuni_ansym_cuni_cnsym_cuni_lnsym_ddef_lnsym_def_lnsym_dia_lnsym_dran_lnsym_end_ansym_exc_lnsym_gau_ansym_hie_lnsym_hienc_lnsym_int_ansym_natd_lnsym_nean_lnsym_nexc_lnsym_nuni_lnsym_opt_lnsym_pes_lnsym_sgau_ansym_uni_ansym_uni_lnsym_usim_lnsymd_gau_ansymd_gimg_ansymd_rpix_ansymd_uni_anugau_bor_lnulap_bor_lnunc_fixw_anunc_vgau_anuncs_guni_cn
Dependencies:C50caretclassclassIntcliclockcodetoolscpp11Cubistdata.tablediagramdigestdplyre1071ExtDistfarverFNNforeachFormulafuturefuture.applygenericsggplot2globalsgluegowergtablehardhatinumipredisobanditeratorsKernSmoothlabelinglatticelavalibcoinlifecyclelistenvlsrlubridatemagrittrMASSMatrixModelMetricsmvtnormnlmenloptrnnetnumDerivoptimxparallellypartykitpillarpkgconfigplyrpracmapROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartRSNNSS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
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 |
