Package 'noisemodel'

Description

Introduction of Asymmetric default label noise into a classification dataset.

Usage

## Default S3 method:
asy_def_ln(x, y, level, def = 1, order = levels(y), sortid = TRUE, ...)

## S3 method for class 'formula'
asy_def_ln(formula, data, ...)

Arguments

Details

Asymmetric default label noise randomly selects (level[i]·100)% of the samples of each class C[i] in the dataset -the order of the class labels is determined by order. Then, the labels of these samples are replaced by a fixed label (C[def]) within the set of class labels.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

R. C. Prati, J. Luengo, and F. Herrera. Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise. Knowledge and Information Systems, 60(1):63–97, 2019. doi:10.1007/s10115-018-1244-4.

See Also

sym_nean_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- asy_def_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                     level = c(0.1, 0.2, 0.3), order = c("virginica", "setosa", "versicolor"))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- asy_def_ln(formula = Species ~ ., data = iris2D, 
                     level = c(0.1, 0.2, 0.3), order = c("virginica", "setosa", "versicolor"))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Asymmetric interval-based attribute noise into a classification dataset.

Usage

## Default S3 method:
asy_int_an(x, y, level, nbins = 10, sortid = TRUE, ...)

## S3 method for class 'formula'
asy_int_an(formula, data, ...)

Arguments

Details

Asymmetric interval-based attribute noise corrupts (level[i]·100)% of the values for each attribute A[i] in the dataset. In order to corrupt an attribute A[i], (level[i]·100)% of the samples in the dataset are chosen. To corrupt a value in numeric attributes, the attribute is split into equal-frequency intervals, one of its closest intervals is picked out and a random valuen within the interval is chosen as noisy. For nominal attributes, a random value within the domain is selected.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

M. V. Mannino, Y. Yang, and Y. Ryu. Classification algorithm sensitivity to training data with non representative attribute noise. Decision Support Systems, 46(3):743-751, 2009. doi:10.1016/j.dss.2008.11.021.

See Also

asy_uni_an, symd_gimg_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- asy_int_an(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                         level = c(0.1, 0.2))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- asy_int_an(formula = Species ~ ., data = iris2D,
                         level = c(0.1, 0.2))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Asymmetric sparse label noise into a classification dataset.

Usage

## Default S3 method:
asy_spa_ln(x, y, levelO, levelE, order = levels(y), sortid = TRUE, ...)

## S3 method for class 'formula'
asy_spa_ln(formula, data, ...)

Arguments

Details

Asymmetric sparse label noise randomly selects (levelO·100)% of the samples in each odd class and (levelE·100)% of the samples in each even class -the order of the class labels is determined by order. Then, each odd class is flipped to the next class, whereas each even class is flipped to the previous class. If the dataset has an odd number of classes, the last class is not corrupted.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

J. Wei and Y. Liu. When optimizing f-divergence is robust with label noise. In Proc. 9th International Conference on Learning Representations, pages 1-11, 2021. url:https://openreview.net/forum?id=WesiCoRVQ15.

See Also

mind_bdir_ln, fra_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- asy_spa_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                       levelO = 0.1, levelE = 0.3, order = c("virginica", "setosa", "versicolor"))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- asy_spa_ln(formula = Species ~ ., data = iris2D, 
                        levelO = 0.1, levelE = 0.3, order = c("virginica", "setosa", "versicolor"))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Asymmetric uniform attribute noise into a classification dataset.

Usage

## Default S3 method:
asy_uni_an(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
asy_uni_an(formula, data, ...)

Arguments

Details

Asymmetric uniform attribute noise corrupts (level[i]·100)% of the values for each attribute A[i] in the dataset. In order to corrupt an attribute A[i], (level[i]·100)% of the samples in the dataset are chosen. Then, their values for A[i] are replaced by random different ones between the minimum and maximum of the domain of the attribute following a uniform distribution (for numerical attributes) or choosing a random value (for nominal attributes).

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

A. Petety, S. Tripathi, and N. Hemachandra. Attribute noise robust binary classification. In Proc. 34th AAAI Conference on Artificial Intelligence, pages 13897-13898, 2020.

See Also

symd_gimg_an, unc_vgau_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- asy_uni_an(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                         level = c(0.1, 0.2))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- asy_uni_an(formula = Species ~ ., data = iris2D,
                         level = c(0.1, 0.2))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Asymmetric uniform label noise into a classification dataset.

Usage

## Default S3 method:
asy_uni_ln(x, y, level, order = levels(y), sortid = TRUE, ...)

## S3 method for class 'formula'
asy_uni_ln(formula, data, ...)

Arguments

Details

Asymmetric uniform label noise randomly selects (level[i]·100)% of the samples of each class C[i] in the dataset -the order of the class labels is determined by order. Finally, the labels of these samples are randomly replaced by other different ones within the set of class labels.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

Z. Zhao, L. Chu, D. Tao, and J. Pei. Classification with label noise: a Markov chain sampling framework. Data Mining and Knowledge Discovery, 33(5):1468-1504, 2019. doi:10.1007/s10618-018-0592-8.

See Also

maj_udir_ln, asy_def_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- asy_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                    level = c(0.1, 0.2, 0.3), order = c("virginica", "setosa", "versicolor"))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- asy_uni_ln(formula = Species ~ ., data = iris2D, 
                     level = c(0.1, 0.2, 0.3), order = c("virginica", "setosa", "versicolor"))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Attribute-mean uniform label noise into a classification dataset.

Usage

## Default S3 method:
attm_uni_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
attm_uni_ln(formula, data, ...)

Arguments

Details

For each sample, its distance to the mean of each attribute is computed. Then, (level·100)% of the samples in the dataset are randomly selected to be mislabeled, more likely choosing samples whose features are generally close to the mean. The labels of these samples are randomly replaced by other different ones within the set of class labels.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References

References

B. Nicholson, V. S. Sheng, and J. Zhang. Label noise correction and application in crowdsourcing. Expert Systems with Applications, 66:149-162, 2016. doi:10.1016/j.eswa.2016.09.003.

See Also

qua_uni_ln, exps_cuni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- attm_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- attm_uni_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Boundary/dependent Gaussian attribute noise into a classification dataset.

Usage

## Default S3 method:
boud_gau_an(x, y, level, k = 0.2, sortid = TRUE, ...)

## S3 method for class 'formula'
boud_gau_an(formula, data, ...)

Arguments

Details

Boundary/dependent Gaussian attribute noise corrupts (level·100)% samples among the ((level+0.1)·100)% of samples closest to the decision boundary. Their attribute values are corrupted by adding a random number that follows a Gaussian distribution of mean = 0 and standard deviation = (max-min)·k, being max and min the limits of the attribute domain. For nominal attributes, a random value is chosen.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

J. Bi and T. Zhang. Support vector classification with input data uncertainty. In Advances in Neural Information Processing Systems, volume 17, pages 161-168, 2004. url:https://proceedings.neurips.cc/paper/2004/hash/22b1f2e0983160db6f7bb9f62f4dbb39-Abstract.html.

See Also

imp_int_an, asy_int_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- boud_gau_an(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- boud_gau_an(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Clustering-based voting label noise into a classification dataset.

Usage

## Default S3 method:
clu_vot_ln(x, y, k = nlevels(y), sortid = TRUE, ...)

## S3 method for class 'formula'
clu_vot_ln(formula, data, ...)

Arguments

Details

Clustering-based voting label noise divides the dataset into k clusters. Then, the labels of each cluster are relabeled with the majority class among its samples.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References, which considers k-means as unsupervised clustering method.

References

Q. Wang, B. Han, T. Liu, G. Niu, J. Yang, and C. Gong. Tackling instance-dependent label noise via a universal probabilistic model. In Proc. 35th AAAI Conference on Artificial Intelligence, pages 10183-10191, 2021. url:https://ojs.aaai.org/index.php/AAAI/article/view/17221.

See Also

sco_con_ln, mis_pre_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- clu_vot_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)])

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- clu_vot_ln(formula = Species ~ ., data = iris2D)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Discretized version of the iris2D dataset.

Usage

data(diris2D)

Format

A data.frame with 103 samples (rows) and 3 variables (columns) named Petal.Length, Petal.Width and Species.

Source

Data collected by E. Anderson (1935).

References

R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179-188, 1936.

E. Anderson. The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59:2-5, 1935.

See Also

iris2D, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(diris2D)

# noise introduction
set.seed(9)
outdef <- sym_uni_ln(x = diris2D[,-ncol(diris2D)], y = diris2D[,ncol(diris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)

Description

Introduction of Exponential borderline label noise into a classification dataset.

Usage

## Default S3 method:
exp_bor_ln(x, y, level, rate = 1, k = 1, sortid = TRUE, ...)

## S3 method for class 'formula'
exp_bor_ln(formula, data, ...)

Arguments

Details

Exponential borderline label noise uses an SVM to induce the decision border in the dataset. For each sample, its distance to the decision border is computed. Then, an exponential distribution with parameter rate is used to compute the value for the probability density function associated to each distance. Finally, (level·100)% of the samples in the dataset are randomly selected to be mislabeled according to their values of the probability density function. For each noisy sample, the majority class among its k-nearest neighbors of a different class is chosen as the new label.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References, considering SVM with linear kernel as classifier, a mislabeling process using the neighborhood of noisy samples and a noise level to control the number of errors in the data.

References

J. Bootkrajang. A generalised label noise model for classification in the presence of annotation errors. Neurocomputing, 192:61–71, 2016. doi:10.1016/j.neucom.2015.12.106.

See Also

pmd_con_ln, clu_vot_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- exp_bor_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- exp_bor_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Exponential/smudge completely-uniform label noise into a classification dataset.

Usage

## Default S3 method:
exps_cuni_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
exps_cuni_ln(formula, data, ...)

Arguments

Details

Exponential/smudge completely-uniform label noise includes an additional attribute (smudge) in the dataset with random values in [0,1]. This attribute is used to compute the mislabeling probability for each sample based on an exponential function (in which level is used as lambda). It selects samples in the dataset based on these probabilities. Finally, the labels of these samples are randomly replaced by others within the set of class labels (this model can choose the original label of a sample as noisy).

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

B. Denham, R. Pears, and M. A. Naeem. Null-labelling: A generic approach for learning in the presence of class noise. In Proc. 20th IEEE International Conference on Data Mining, pages 990–995, 2020. doi:10.1109/ICDM50108.2020.00114.

See Also

opes_idu_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- exps_cuni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.8)

# show results
summary(outdef, showid = TRUE)
plot(outdef, pca = TRUE)

# usage of the method for class formula
set.seed(9)
outfrm <- exps_cuni_ln(formula = Species ~ ., data = iris2D, level = 0.8)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Fraud bidirectional label noise into a classification dataset.

Usage

## Default S3 method:
fra_bdir_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
fra_bdir_ln(formula, data, ...)

Arguments

Details

Fraud bidirectional label noise randomly selects (level·100)% of the samples from the minority class in the dataset and level·10 samples from the majority class. Then, minority class samples are mislabeled as belonging to the majority class and majority class samples are mislabeled as belonging to the minority class. In case of ties determining minority and majority classes, a random class is chosen among them.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

Z. Salekshahrezaee, J. L. Leevy, and T. M. Khoshgoftaar. A reconstruction error-based framework for label noise detection. Journal of Big Data, 8(1):1-16, 2021. doi:10.1186/s40537-021-00447-5.

See Also

irs_bdir_ln, pai_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- fra_bdir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- fra_bdir_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Gamma borderline label noise into a classification dataset.

Usage

## Default S3 method:
gam_bor_ln(x, y, level, shape = 1, rate = 0.5, k = 1, sortid = TRUE, ...)

## S3 method for class 'formula'
gam_bor_ln(formula, data, ...)

Arguments

Details

Gamma borderline label noise uses an SVM to induce the decision border in the dataset. For each sample, its distance to the decision border is computed. Then, a gamma distribution with parameters (shape, rate) is used to compute the value for the probability density function associated to each distance. Finally, (level·100)% of the samples in the dataset are randomly selected to be mislabeled according to their values of the probability density function. For each noisy sample, the majority class among its k-nearest neighbors of a different class is chosen as the new label.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References, considering SVM with linear kernel as classifier, a mislabeling process using the neighborhood of noisy samples and a noise level to control the number of errors in the data.

References

J. Bootkrajang. A generalised label noise model for classification. In Proc. 23rd European Symposium on Artificial Neural Networks, pages 349-354, 2015. url:https://dblp.org/rec/conf/esann/Bootkrajang15.html?view=bibtex.

See Also

exp_bor_ln, pmd_con_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- gam_bor_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- gam_bor_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Gaussian borderline label noise into a classification dataset.

Usage

## Default S3 method:
gau_bor_ln(x, y, level, mean = 0, sd = 1, k = 1, sortid = TRUE, ...)

## S3 method for class 'formula'
gau_bor_ln(formula, data, ...)

Arguments

Details

Gaussian borderline label noise uses an SVM to induce the decision border in the dataset. For each sample, its distance to the decision border is computed. Then, a Gaussian distribution with parameters (mean, sd) is used to compute the value for the probability density function associated to each distance. Finally, (level·100)% of the samples in the dataset are randomly selected to be mislabeled according to their values of the probability density function. For each noisy sample, the majority class among its k-nearest neighbors of a different class is chosen as the new label.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References to multiclass data, considering SVM with linear kernel as classifier, a mislabeling process using the neighborhood of noisy samples and a noise level to control the number of errors in the data.

References

J. Bootkrajang and J. Chaijaruwanich. Towards instance-dependent label noise-tolerant classification: a probabilistic approach. Pattern Analysis and Applications, 23(1):95-111, 2020. doi:10.1007/s10044-018-0750-z.

See Also

sigb_uni_ln, larm_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- gau_bor_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- gau_bor_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Gaussian-mixture borderline label noise into a classification dataset.

Usage

## Default S3 method:
gaum_bor_ln(
  x,
  y,
  level,
  mean = c(0, 2),
  sd = c(sqrt(0.5), sqrt(0.5)),
  w = c(0.5, 0.5),
  k = 1,
  sortid = TRUE,
  ...
)

## S3 method for class 'formula'
gaum_bor_ln(formula, data, ...)

Arguments

Details

Gaussian-mixture borderline label noise uses an SVM to induce the decision border in the dataset. For each sample, its distance to the decision border is computed. Then, a Gaussian mixture distribution with parameters (mean, sd) and weights w is used to compute the value for the probability density function associated to each distance. Finally, (level·100)% of the samples in the dataset are randomly selected to be mislabeled according to their values of the probability density function. For each noisy sample, the majority class among its k-nearest neighbors of a different class is chosen as the new label.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References, considering SVM with linear kernel as classifier, a mislabeling process using the neighborhood of noisy samples and a noise level to control the number of errors in the data.

References

J. Bootkrajang and J. Chaijaruwanich. Towards instance-dependent label noise-tolerant classification: a probabilistic approach. Pattern Analysis and Applications, 23(1):95-111, 2020. doi:10.1007/s10044-018-0750-z.

See Also

gau_bor_ln, sigb_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- gaum_bor_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- gaum_bor_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Gaussian-level uniform label noise into a classification dataset.

Usage

## Default S3 method:
glev_uni_ln(x, y, level, sd = 0.01, sortid = TRUE, ...)

## S3 method for class 'formula'
glev_uni_ln(formula, data, ...)

Arguments

Details

For each sample, Gaussian-level uniform label noise assigns a random probability following a Gaussian distribution of mean = level and standard deviation sd. Noisy samples are chosen according to these probabilities. The labels of these samples are randomly replaced by other different ones within the set of class labels.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

D. Liu, G. Yang, J. Wu, J. Zhao, and F. Lv. Robust binary loss for multi-category classification with label noise. In Proc. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 1700-1704, 2021. doi:10.1109/ICASSP39728.2021.9414493.

See Also

sym_hienc_ln, sym_nexc_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- glev_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- glev_uni_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Hubness-proportional uniform label noise into a classification dataset.

Usage

## Default S3 method:
hubp_uni_ln(x, y, level, k = 3, sortid = TRUE, ...)

## S3 method for class 'formula'
hubp_uni_ln(formula, data, ...)

Arguments

Details

Hubness-proportional uniform label noise is based on the presence of hubs in the dataset. It selects (level·100)% of the samples in the dataset using a discrete probability distribution based on the concept of hubness, which is computed using the nearest neighbors of each sample. Then, the class labels of these samples are randomly replaced by different ones from the c classes.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

N. Tomasev and K. Buza. Hubness-aware kNN classification of high-dimensional data in presence of label noise. Neurocomputing, 160:157-172, 2015. doi:10.1016/j.neucom.2014.10.084.

See Also

smu_cuni_ln, oned_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- hubp_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- hubp_uni_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Importance interval-based attribute noise into a classification dataset.

Usage

## Default S3 method:
imp_int_an(x, y, level, nbins = 10, ascending = TRUE, sortid = TRUE, ...)

## S3 method for class 'formula'
imp_int_an(formula, data, ...)

Arguments

Details

The values in level are ordered and assigned to attributes according to their information gain (using the ordering given by ascending). Then, Importance interval-based attribute noise corrupts (level[i]·100)% of the values for each attribute A[i] in the dataset. In order to corrupt each attribute A[i], (level[i]·100)% of the samples in the dataset are chosen. To corrupt a value in numeric attributes, the attribute is split into equal-frequency intervals, one of its closest intervals is picked out and a random value within the interval is chosen as noisy. For nominal attributes, a random value within the domain is chosen.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

M. V. Mannino, Y. Yang, and Y. Ryu. Classification algorithm sensitivity to training data with non representative attribute noise. Decision Support Systems, 46(3):743-751, 2009. doi:10.1016/j.dss.2008.11.021.

See Also

asy_int_an, asy_uni_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- imp_int_an(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                        level = c(0.1, 0.2))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- imp_int_an(formula = Species ~ ., data = iris2D, 
                        level = c(0.1, 0.2))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

A 2-dimensional version of the well-known iris dataset. It maintains the attributes Petal.Length and Petal.Width, which give the measurements in centimeters of the petal length and width of iris flowers belonging to three different species (setosa, versicolor and virginica). Duplicate and contradictory samples are removed from the dataset, resulting in a total of 103 samples.

Usage

data(iris2D)

Format

A data.frame with 103 samples (rows) and 3 variables (columns) named Petal.Length, Petal.Width and Species.

Source

Data collected by E. Anderson (1935).

References

R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179-188, 1936.

E. Anderson. The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59:2-5, 1935.

See Also

sym_uni_ln, sym_uni_an, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

library(ggplot2)
library(RColorBrewer)

data(iris2D)

ggplot(data = iris2D, aes(x = iris2D[,1], y = iris2D[,2], color = iris2D[,3])) +
   geom_point(stroke = 0.5) +
   xlim(min(iris2D[,1]), max(iris2D[,1])) +
   ylim(min(iris2D[,2]), max(iris2D[,2])) +
   xlab(names(iris2D)[1]) + 
   ylab(names(iris2D)[2]) +
   labs(color='Species') +
   scale_color_manual(values = brewer.pal(3, "Dark2")) +
   theme(panel.border = element_rect(colour = "black", fill=NA),
         aspect.ratio = 1,
         axis.text = element_text(colour = 1, size = 12),
         legend.background = element_blank(),
         legend.box.background = element_rect(colour = "black"))

Description

Introduction of IR-stable bidirectional label noise into a classification dataset.

Usage

## Default S3 method:
irs_bdir_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
irs_bdir_ln(formula, data, ...)

Arguments

Details

IR-stable bidirectional label noise randomly selects (level·100)% of the samples from the minority class in the dataset and the same amount of samples from the majority class. Then, minority class samples are mislabeled as belonging to the majority class and majority class samples are mislabeled as belonging to the minority class. In case of ties determining minority and majority classes, a random class is chosen among them.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

B. Chen, S. Xia, Z. Chen, B. Wang, and G. Wang. RSMOTE: A self-adaptive robust SMOTE for imbalanced problems with label noise. Information Sciences, 553:397-428, 2021. doi:10.1016/j.ins.2020.10.013.

See Also

pai_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- irs_bdir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- irs_bdir_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Laplace borderline label noise into a classification dataset.

Usage

## Default S3 method:
lap_bor_ln(x, y, level, mu = 0, b = 1, k = 1, sortid = TRUE, ...)

## S3 method for class 'formula'
lap_bor_ln(formula, data, ...)

Arguments

Details

Laplace borderline label noise uses uses an SVM to induce the decision border in the dataset. For each sample, its distance to the decision border is computed. Then, a Laplace distribution with parameters (mu, b) is used to compute the value for the probability density function associated to each distance. Finally, (level·100)% of the samples in the dataset are randomly selected to be mislabeled according to their values of the probability density function. For each noisy sample, the majority class among its k-nearest neighbors of a different class is chosen as the new label.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References to multiclass data, considering SVM with linear kernel as classifier, a mislabeling process using the neighborhood of noisy samples and a noise level to control the number of errors in the data.

References

J. Du and Z. Cai. Modelling class noise with symmetric and asymmetric distributions. In Proc. 29th AAAI Conference on Artificial Intelligence, pages 2589-2595, 2015. url:https://dl.acm.org/doi/10.5555/2886521.2886681.

See Also

ugau_bor_ln, gaum_bor_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- lap_bor_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- lap_bor_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Large-margin uniform label noise into a classification dataset.

Usage

## Default S3 method:
larm_uni_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
larm_uni_ln(formula, data, ...)

Arguments

Details

Large-margin uniform label noise uses an SVM to induce the decision border in the dataset. For each sample, its distance to the decision border is computed. Then, the samples are ordered according to their distance and (level·100)% of the most distant correctly classified samples to the decision boundary are selected to be mislabeled with a random different class.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References to multiclass data, considering SVM with linear kernel as classifier.

References

E. Amid, M. K. Warmuth, and S. Srinivasan. Two-temperature logistic regression based on the Tsallis divergence. In Proc. 22nd International Conference on Artificial Intelligence and Statistics, volume 89 of PMLR, pages 2388-2396, 2019. url:http://proceedings.mlr.press/v89/amid19a.html.

See Also

hubp_uni_ln, smu_cuni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- larm_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.3)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- larm_uni_ln(formula = Species ~ ., data = iris2D, level = 0.3)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Majority-class unidirectional label noise into a classification dataset.

Usage

## Default S3 method:
maj_udir_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
maj_udir_ln(formula, data, ...)

Arguments

Details

Let A be the majority class and B be the second majority class in the dataset. The Majority-class unidirectional label noise introduction model randomly selects (level·100)% of the samples of A and labels them as B.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References to multiclass data.

References

J. Li, Q. Zhu, Q. Wu, Z. Zhang, Y. Gong, Z. He, and F. Zhu. SMOTE- NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution. Knowledge-Based Systems, 223:107056, 2021. doi:10.1016/j.knosys.2021.107056.

See Also

asy_def_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- maj_udir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- maj_udir_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Minority-driven bidirectional label noise into a classification dataset.

Usage

## Default S3 method:
mind_bdir_ln(x, y, level, pos = 0.1, sortid = TRUE, ...)

## S3 method for class 'formula'
mind_bdir_ln(formula, data, ...)

Arguments

Details

Minority-driven bidirectional label noise randomly selects n = 2m·level samples in the dataset (with m the number of samples in the minority class), making sure that n·pos samples belong to the minority class and the rest to the majority class. Then, minority class samples are mislabeled as belonging to the majority class and majority class samples are mislabeled as belonging to the minority class. In case of ties determining minority and majority classes, a random class is chosen among them.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References to multiclass data.

References

A. Folleco, T. M. Khoshgoftaar, J. V. Hulse, and L. A. Bullard. Software quality modeling: The impact of class noise on the random forest classifier. In Proc. 2008 IEEE Congress on Evolutionary Computation, pages 3853–3859, 2008. doi:10.1109/CEC.2008.4631321.

See Also

fra_bdir_ln, irs_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- mind_bdir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.5)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- mind_bdir_ln(formula = Species ~ ., data = iris2D, level = 0.5)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Minority-proportional uniform label noise into a classification dataset.

Usage

## Default S3 method:
minp_uni_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
minp_uni_ln(formula, data, ...)

Arguments

Details

Given a dataset, assume the original class distribution of class i is pi and the distribution of the minority class is pm. Let level be the noise level, Minority-proportional uniform label noise introduces noise proportionally to different classes, where a sample with its label i has a probability (pm/pi)·level to be corrupted as another random class. That is, the least common class is used as the baseline for noise introduction.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

X. Zhu and X. Wu. Cost-guided class noise handling for effective cost-sensitive learning. In Proc. 4th IEEE International Conference on Data Mining, pages 297–304, 2004. doi:10.1109/ICDM.2004.10108.

See Also

asy_uni_ln, maj_udir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- minp_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- minp_uni_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Misclassification prediction label noise into a classification dataset.

Usage

## Default S3 method:
mis_pre_ln(x, y, sortid = TRUE, ...)

## S3 method for class 'formula'
mis_pre_ln(formula, data, ...)

Arguments

Details

Misclassification prediction label noise creates a Multi-Layer Perceptron (MLP) model from the dataset and relabels each sample with the class predicted by the classifier.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

Q. Wang, B. Han, T. Liu, G. Niu, J. Yang, and C. Gong. Tackling instance-dependent label noise via a universal probabilistic model. In Proc. 35th AAAI Conference on Artificial Intelligence, pages 10183-10191, 2021. url:https://ojs.aaai.org/index.php/AAAI/article/view/17221.

See Also

smam_bor_ln, nlin_bor_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- mis_pre_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)])

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- mis_pre_ln(formula = Species ~ ., data = iris2D)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Multiple-class unidirectional label noise into a classification dataset.

Usage

## Default S3 method:
mulc_udir_ln(x, y, level, goal, order = levels(y), sortid = TRUE, ...)

## S3 method for class 'formula'
mulc_udir_ln(formula, data, ...)

Arguments

Details

Multiple-class unidirectional label noise introduction model randomly selects (level·100)% of the samples of each class c with goal[c] != NA. Then, the labels c of these samples are replaced by the class indicated in goal[c]. The order of indices in goal is determined by order.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

Q. Wang, B. Han, T. Liu, G. Niu, J. Yang, and C. Gong. Tackling instance-dependent label noise via a universal probabilistic model. In Proc. 35th AAAI Conference on Artificial Intelligence, pages 10183-10191, 2021. url:https://ojs.aaai.org/index.php/AAAI/article/view/17221.

See Also

minp_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- mulc_udir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1, 
                        goal = c(NA, 1, 2), order = c("virginica", "setosa", "versicolor"))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- mulc_udir_ln(formula = Species ~ ., data = iris2D, level = 0.1, 
                        goal = c(NA, 1, 2), order = c("virginica", "setosa", "versicolor"))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Neighborwise borderline label noise into a classification dataset.

Usage

## Default S3 method:
nei_bor_ln(x, y, level, k = 1, sortid = TRUE, ...)

## S3 method for class 'formula'
nei_bor_ln(formula, data, ...)

Arguments

Details

For each sample in the dataset, Neighborwise borderline label noise computes the ratio of two distances: the distance to its nearest neighbor from the same class and the distance to its nearest neighbor from another class. Then, these values are ordered in descending order and the first (level·100)% of them are used to determine the noisy samples. For each noisy sample, the majority class among its k-nearest neighbors of a different class is chosen as the new label.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References, considering a mislabeling process using the neighborhood of noisy samples.

References

L. P. F. Garcia, J. Lehmann, A. C. P. L. F. de Carvalho, and A. C. Lorena. New label noise injection methods for the evaluation of noise filters. Knowledge-Based Systems, 163:693–704, 2019. doi:10.1016/j.knosys.2018.09.031.

See Also

ulap_bor_ln, lap_bor_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- nei_bor_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- nei_bor_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Non-linearwise borderline label noise into a classification dataset.

Usage

## Default S3 method:
nlin_bor_ln(x, y, level, k = 1, sortid = TRUE, ...)

## S3 method for class 'formula'
nlin_bor_ln(formula, data, ...)

Arguments

Details

Non-linearwise borderline label noise uses an SVM to induce the decision border in the dataset. Then, for each sample, its distance to the decision border is computed. Finally, the distances obtained are ordered in ascending order and the first (level·100)% of them are used to determine the noisy samples. For each noisy sample, the majority class among its k-nearest neighbors of a different class is chosen as the new label.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References, considering a mislabeling process using the neighborhood of noisy samples.

References

L. P. F. Garcia, J. Lehmann, A. C. P. L. F. de Carvalho, and A. C. Lorena. New label noise injection methods for the evaluation of noise filters. Knowledge-Based Systems, 163:693–704, 2019. doi:10.1016/j.knosys.2018.09.031.

See Also

nei_bor_ln, ulap_bor_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- nlin_bor_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- nlin_bor_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of One-dimensional uniform label noise into a classification dataset.

Usage

## Default S3 method:
oned_uni_ln(
  x,
  y,
  level,
  att,
  lower,
  upper,
  order = levels(y),
  sortid = TRUE,
  ...
)

## S3 method for class 'formula'
oned_uni_ln(formula, data, ...)

Arguments

Details

One-dimensional uniform label noise is based on the introduction of noise according to the values of the attribute att. Samples of class i with the attribute att falling between lower[i] and upper[i] have a probability level of being mislabeled. The labels of these samples are randomly replaced by other different ones within the set of class labels. The order of the class labels is determined by order.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References to multiclass data, considering a noise level to control the number of errors in the data

References

N. Gornitz, A. Porbadnigk, A. Binder, C. Sannelli, M. L. Braun, K. Muller, and M. Kloft. Learning and evaluation in presence of non-i.i.d. label noise. In Proc. 17th International Conference on Artificial Intelligence and Statistics, volume 33 of PMLR, pages 293–302, 2014. url:https://proceedings.mlr.press/v33/gornitz14.html.

See Also

attm_uni_ln, qua_uni_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- oned_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                    level = 0.5, att = 1, lower = c(1.5,2,6), upper = c(2,4,7))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- oned_uni_ln(formula = Species ~ ., data = iris2D, 
                    level = 0.5, att = 1, lower = c(1.5,2,6), upper = c(2,4,7))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Open-set ID/nearest-neighbor label noise into a classification dataset.

Usage

## Default S3 method:
opes_idnn_ln(
  x,
  y,
  level,
  openset = c(1),
  order = levels(y),
  sortid = TRUE,
  ...
)

## S3 method for class 'formula'
opes_idnn_ln(formula, data, ...)

Arguments

Details

Open-set ID/nearest-neighbor label noise corrupts (level·100)% of the samples with classes in openset. Then, the labels of these samples are replaced by the label of the nearest sample of a different in-distribution class. The order of the class labels for the indices in openset is determined by order.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

P. H. Seo, G. Kim, and B. Han. Combinatorial inference against label noise. In Advances in Neural Information Processing Systems, volume 32, pages 1171-1181, 2019. url:https://proceedings.neurips.cc/paper/2019/hash/0cb929eae7a499e50248a3a78f7acfc7-Abstract.html.

See Also

opes_idu_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- opes_idnn_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                      level = 0.4, order = c("virginica", "setosa", "versicolor"))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- opes_idnn_ln(formula = Species ~ ., data = iris2D, 
                      level = 0.4, order = c("virginica", "setosa", "versicolor"))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Open-set ID/uniform label noise into a classification dataset.

Usage

## Default S3 method:
opes_idu_ln(x, y, level, openset = c(1), order = levels(y), sortid = TRUE, ...)

## S3 method for class 'formula'
opes_idu_ln(formula, data, ...)

Arguments

Details

Open-set ID/uniform label noise corrupts (level·100)% of the samples with classes in openset. For each sample selected, a label from in-distribution classes is randomly chosen. The order of the class labels for the indices in openset is determined by order.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

P. H. Seo, G. Kim, and B. Han. Combinatorial inference against label noise. In Advances in Neural Information Processing Systems, volume 32, pages 1171-1181, 2019. url:https://proceedings.neurips.cc/paper/2019/hash/0cb929eae7a499e50248a3a78f7acfc7-Abstract.html.

See Also

asy_spa_ln, mind_bdir_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- opes_idu_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                     level = 0.4, order = c("virginica", "setosa", "versicolor"))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- opes_idu_ln(formula = Species ~ ., data = iris2D, 
                     level = 0.4, order = c("virginica", "setosa", "versicolor"))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Introduction of Pairwise bidirectional label noise into a classification dataset.

Usage

## Default S3 method:
pai_bdir_ln(x, y, level, pairs, order = levels(y), sortid = TRUE, ...)

## S3 method for class 'formula'
pai_bdir_ln(formula, data, ...)

Arguments

Details

For each vector (c1, c2) in pairs, Pairwise bidirectional label noise randomly selects (level·100)% of the samples from class c1 in the dataset and (level·100)% of the samples from class c2. Then, c1 samples are mislabeled as belonging to c2 and c2 samples are mislabeled as belonging to c1. The order of the class labels is determined by order.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

S. Fefilatyev, M. Shreve, K. Kramer, L. O. Hall, D. B. Goldgof, R. Kasturi, K. Daly, A. Remsen, and H. Bunke. Label-noise reduction with support vector machines. In Proc. 21st International Conference on Pattern Recognition, pages 3504-3508, 2012. url:https://ieeexplore.ieee.org/document/6460920/.

See Also

print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# create new class with some samples
class <- as.character(iris2D$Species)
class[iris2D$Petal.Length > 6] <- "newclass"
iris2D$Species <- as.factor(class)

# usage of the default method
set.seed(9)
outdef <- pai_bdir_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], 
                      level = 0.1, pairs = list(c(1,2), c(3,4)), 
                      order = c("virginica", "setosa", "newclass", "versicolor"))

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- pai_bdir_ln(formula = Species ~ ., data = iris2D, 
                      level = 0.1, pairs = list(c(1,2), c(3,4)), 
                      order = c("virginica", "setosa", "newclass", "versicolor"))

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

Representation of the dataset contained in an object of class ndmodel after the application of a noise introduction model.

Usage

## S3 method for class 'ndmodel'
plot(x, ..., noise = NA, xvar = 1, yvar = 2, pca = FALSE)

Arguments

Details

This function performs a two-dimensional representation using the ggplot2 package of the dataset contained in the object x of class ndmodel. Each of the classes in the dataset (available in x$ynoise) is represented by a different color. There are two options to represent the input attributes of the samples on the x and y axes of the graph:

• If pca = FALSE, the values in the graph are taken from the current attribute values found in x$xnoise. In this case, xvar and yvar indicate the indices of the attributes to show in the x and y axes, respectively.

• If pca = TRUE, the values in the graph are taken after performing a PCA over x$xnoise. In this case, xvar and yvar indicate the index of the principal component according to the variance explained to show in the x and y axes, respectively.

Finally, the parameter noise is used to indicate which samples (noisy, clean or all) to show. Clean samples are represented by circles in the graph, while noisy samples are represented by crosses.

Value

An object of class ggplot and gg with the graph created using the ggplot2 package.

See Also

print.ndmodel, summary.ndmodel, sym_uni_ln, sym_cuni_ln, sym_uni_an

Examples

# load the dataset
data(iris)

# apply the noise introduction model
set.seed(9)
output <- sym_uni_ln(x = iris[,-ncol(iris)], y = iris[,ncol(iris)], level = 0.1)

# plots for all the samples, the clean samples and the noisy samples using PCA
plot(output, pca = TRUE)
plot(output, noise = FALSE, pca = TRUE)
plot(output, noise = TRUE, pca = TRUE)

# plots using the Petal.Length and Petal.Width variables
plot(output, xvar = 3, yvar = 4)
plot(output, noise = FALSE, xvar = 3, yvar = 4)
plot(output, noise = TRUE, xvar = 3, yvar = 4)

Description

Introduction of PMD-based confidence label noise into a classification dataset.

Usage

## Default S3 method:
pmd_con_ln(x, y, level, sortid = TRUE, ...)

## S3 method for class 'formula'
pmd_con_ln(formula, data, ...)

Arguments

Details

PMD-based confidence label noise approximates the probability of noise using the confidence prediction of a neural network. These predictions are used to estimate the mislabeling probability and the most possible noisy class label for each sample. Finally, (level·100)% of the samples in the dataset are randomly selected to be mislabeled according to their values of probability computed.

Value

An object of class ndmodel with elements:

Note

Noise model adapted from the papers in References.

References

Y. Zhang, S. Zheng, P. Wu, M. Goswami, and C. Chen. Learning with feature-dependent label noise: A progressive approach. In Proc. 9th International Conference on Learning Representations, pages 1-13, 2021. url:https://openreview.net/forum?id=ZPa2SyGcbwh.

See Also

clu_vot_ln, sco_con_ln, print.ndmodel, summary.ndmodel, plot.ndmodel

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- pmd_con_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
summary(outdef, showid = TRUE)
plot(outdef)

# usage of the method for class formula
set.seed(9)
outfrm <- pmd_con_ln(formula = Species ~ ., data = iris2D, level = 0.1)

# check the match of noisy indices
identical(outdef$idnoise, outfrm$idnoise)

Description

This method displays the basic information about the noise introduction process contained in an object of class ndmodel.

Usage

## S3 method for class 'ndmodel'
print(x, ...)

Arguments

Details

This function presents the basic information of the noise introduction process and the resulting noisy dataset contained in the object x of class ndmodel. The information offered is as follows:

• the name of the noise introduction model.

• the parameters associated with the noise model.

• the number of noisy and clean samples in the dataset.

Value

This function does not return any value.

See Also

summary.ndmodel, plot.ndmodel, sym_uni_ln, sym_cuni_ln, sym_uni_an

Examples

# load the dataset
data(iris2D)

# usage of the default method
set.seed(9)
outdef <- sym_uni_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)

# show results
print(outdef)

Description

Introduction of Quadrant-based uniform label noise into a classification dataset.

Usage

## Default S3 method:
qua_uni_ln(x, y, level, att1 = 1, att2 = 2, sortid = TRUE, ...)

## S3 method for class 'formula'
qua_uni_ln(formula, data, ...)