{ "_id": "6a1f09d1b401979e7341c412", "Package": "noisemodel", "Version": "1.0.2", "Title": "Noise Models for Classification Datasets", "Authors@R": "person(given = \"José A.\",\nfamily = \"Sáez\",\nrole = c(\"aut\", \"cre\"),\nemail = \"joseasaezm@ugr.es\")", "Description": "Implementation of models for the controlled introduction\nof errors in classification datasets. This package contains the\nnoise models described in Saez (2022)\n that allow corrupting class labels,\nattributes and both simultaneously.", "License": "GPL (>= 3)", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "RoxygenNote": "7.2.1", "NeedsCompilation": "no", "Author": "José A. Sáez [aut, cre]", "Maintainer": "José A. 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"table": true, "tojson": true }, { "name": "iris2D", "title": "iris2D dataset", "object": "iris2D", "file": "iris2D.RData", "class": [ "data.frame" ], "fields": [ "Petal.Length", "Petal.Width", "Species" ], "rows": 101, "table": true, "tojson": true } ], "_help": [ { "page": "asy_def_ln", "title": "Asymmetric default label noise", "topics": [ "asy_def_ln", "asy_def_ln.default", "asy_def_ln.formula" ] }, { "page": "asy_int_an", "title": "Asymmetric interval-based attribute noise", "topics": [ "asy_int_an", "asy_int_an.default", "asy_int_an.formula" ] }, { "page": "asy_spa_ln", "title": "Asymmetric sparse label noise", "topics": [ "asy_spa_ln", "asy_spa_ln.default", "asy_spa_ln.formula" ] }, { "page": "asy_uni_an", "title": "Asymmetric uniform attribute noise", "topics": [ "asy_uni_an", "asy_uni_an.default", "asy_uni_an.formula" ] }, { "page": "asy_uni_ln", "title": "Asymmetric uniform label noise", "topics": [ "asy_uni_ln", "asy_uni_ln.default", "asy_uni_ln.formula" ] }, { "page": 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bidirectional label noise", "topics": [ "irs_bdir_ln", "irs_bdir_ln.default", "irs_bdir_ln.formula" ] }, { "page": "lap_bor_ln", "title": "Laplace borderline label noise", "topics": [ "lap_bor_ln", "lap_bor_ln.default", "lap_bor_ln.formula" ] }, { "page": "larm_uni_ln", "title": "Large-margin uniform label noise", "topics": [ "larm_uni_ln", "larm_uni_ln.default", "larm_uni_ln.formula" ] }, { "page": "maj_udir_ln", "title": "Majority-class unidirectional label noise", "topics": [ "maj_udir_ln", "maj_udir_ln.default", "maj_udir_ln.formula" ] }, { "page": "mind_bdir_ln", "title": "Minority-driven bidirectional label noise", "topics": [ "mind_bdir_ln", "mind_bdir_ln.default", "mind_bdir_ln.formula" ] }, { "page": "minp_uni_ln", "title": "Minority-proportional uniform label noise", "topics": [ "minp_uni_ln", "minp_uni_ln.default", "minp_uni_ln.formula" ] }, { "page": "mis_pre_ln", "title": "Misclassification prediction label noise", "topics": [ "mis_pre_ln", "mis_pre_ln.default", 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label noise", "topics": [ "pai_bdir_ln", "pai_bdir_ln.default", "pai_bdir_ln.formula" ] }, { "page": "plot.ndmodel", "title": "Plot function for class ndmodel", "topics": [ "plot.ndmodel" ] }, { "page": "pmd_con_ln", "title": "PMD-based confidence label noise", "topics": [ "pmd_con_ln", "pmd_con_ln.default", "pmd_con_ln.formula" ] }, { "page": "print.ndmodel", "title": "Print function for class ndmodel", "topics": [ "print.ndmodel" ] }, { "page": "qua_uni_ln", "title": "Quadrant-based uniform label noise", "topics": [ "qua_uni_ln", "qua_uni_ln.default", "qua_uni_ln.formula" ] }, { "page": "sco_con_ln", "title": "Score-based confidence label noise", "topics": [ "sco_con_ln", "sco_con_ln.default", "sco_con_ln.formula" ] }, { "page": "sigb_uni_ln", "title": "Sigmoid-bounded uniform label noise", "topics": [ "sigb_uni_ln", "sigb_uni_ln.default", "sigb_uni_ln.formula" ] }, { "page": "smam_bor_ln", "title": "Small-margin borderline label noise", "topics": [ "smam_bor_ln", "smam_bor_ln.default", "smam_bor_ln.formula" ] }, { "page": "smu_cuni_ln", "title": "Smudge-based completely-uniform label noise", "topics": [ "smu_cuni_ln", "smu_cuni_ln.default", "smu_cuni_ln.formula" ] }, { "page": "summary.ndmodel", "title": "Summary function for class ndmodel", "topics": [ "summary.ndmodel" ] }, { "page": "sym_adj_ln", "title": "Symmetric adjacent label noise", "topics": [ "sym_adj_ln", "sym_adj_ln.default", "sym_adj_ln.formula" ] }, { "page": "sym_cen_ln", "title": "Symmetric center-based label noise", "topics": [ "sym_cen_ln", "sym_cen_ln.default", "sym_cen_ln.formula" ] }, { "page": "sym_con_ln", "title": "Symmetric confusion label noise", "topics": [ "sym_con_ln", "sym_con_ln.default", "sym_con_ln.formula" ] }, { "page": "sym_cuni_an", "title": "Symmetric completely-uniform attribute noise", "topics": [ "sym_cuni_an", "sym_cuni_an.default", "sym_cuni_an.formula" ] }, { "page": "sym_cuni_cn", "title": "Symmetric completely-uniform combined noise", "topics": [ 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exchange label noise", "topics": [ "sym_exc_ln", "sym_exc_ln.default", "sym_exc_ln.formula" ] }, { "page": "sym_gau_an", "title": "Symmetric Gaussian attribute noise", "topics": [ "sym_gau_an", "sym_gau_an.default", "sym_gau_an.formula" ] }, { "page": "sym_hie_ln", "title": "Symmetric hierarchical label noise", "topics": [ "sym_hie_ln", "sym_hie_ln.default", "sym_hie_ln.formula" ] }, { "page": "sym_hienc_ln", "title": "Symmetric hierarchical/next-class label noise", "topics": [ "sym_hienc_ln", "sym_hienc_ln.default", "sym_hienc_ln.formula" ] }, { "page": "sym_int_an", "title": "Symmetric interval-based attribute noise", "topics": [ "sym_int_an", "sym_int_an.default", "sym_int_an.formula" ] }, { "page": "sym_natd_ln", "title": "Symmetric natural-distribution label noise", "topics": [ "sym_natd_ln", "sym_natd_ln.default", "sym_natd_ln.formula" ] }, { "page": "sym_nean_ln", "title": "Symmetric nearest-neighbor label noise", "topics": [ "sym_nean_ln", "sym_nean_ln.default", 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