Package 'evtclass'

GEV Classifier - testing

Description

This function is used to evaluate a test set for a pre-trained GEV classifier. It can be used to perform open set classification based on the generalized Pareto distribution.

Usage

gevcTest(train, test, pre, prob = TRUE, alpha)

Arguments

train	a data matrix containing the train data. Class labels should not be included.
test	a data matrix containing the test data.
pre	a numeric vector of parameters obtained with the function gevcTrain.
prob	logical indicating whether p-values should be returned.
alpha	threshold to be used if prob is equal to FALSE. It must be between 0 and 1.

Details

For details on the method and parameters see Vignotto and Engelke (2018).

Value

If prob is equal to TRUE, a vector containing the p-values for each point is returned. A high p-value results in the classification of the corresponding test data as a known point, since this hypothesis cannot be rejected. If the p-value is small, the corresponding test data is classified as an unknown point. If prob is equal to TRUE, a vector of predicted values is returned.

Author(s)

Edoardo Vignotto
[email protected]

References

Vignotto, E., & Engelke, S. (2018). Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers. arXiv preprint arXiv:1808.09902.

See Also

gevcTrain

Examples

trainset <- LETTER[1:15000,]
testset <- LETTER[-(1:15000), -1]
knowns <- trainset[trainset$class==1, -1]
gevClassifier <- gevcTrain(train = knowns)
predicted <- gevcTest(train = knowns, test = testset, pre = gevClassifier)

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GEV Classifier - training

Description

This function is used to train a GEV classifier. It can be used to perform open set classification based on the generalized extreme value distribution.

Usage

gevcTrain(train)

Arguments

train

a data matrix containing the train data. Class labels should not be included.

Details

For details on the method and parameters see Vignotto and Engelke (2018).

Value

A numeric vector of two elements containing the estimated parameters of the fitted reversed Weibull.

Note

Data are not scaled internally; any preprocessing has to be done externally.

Author(s)

Edoardo Vignotto
[email protected]

References

Vignotto, E., & Engelke, S. (2018). Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers. arXiv preprint arXiv:1808.09902.

See Also

gevcTest

Examples

trainset <- LETTER[1:15000,]
knowns <- trainset[trainset$class==1, -1]
gevClassifier <- gevcTrain(train = knowns)

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GPD Classifier - testing

Description

This function is used to evaluate a test set for a pre-trained GPD classifier. It can be used to perform open set classification based on the generalized Pareto distribution.

Usage

gpdcTest(train, test, pre, prob = TRUE, alpha = 0.01)

Arguments

train	data matrix containing the train data. Class labels should not be included.
test	a data matrix containing the test data.
pre	a list obtained with the function gpdcTrain.
prob	logical indicating whether p-values should be returned.
alpha	threshold to be used if prob is equal to FALSE. It must be between 0 and 1.

Details

For details on the method and parameters see Vignotto and Engelke (2018).

Value

If prob is equal to TRUE, a vector containing the p-values for each point is returned. A high p-value results in the classification of the corresponding test data as a known point, since this hypothesis cannot be rejected. If the p-value is small, the corresponding test data is classified as an unknown point. If prob is equal to TRUE, a vector of predicted values is returned.

Author(s)

Edoardo Vignotto
[email protected]

References

Vignotto, E., & Engelke, S. (2018). Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers. arXiv preprint arXiv:1808.09902.

See Also

gpdcTrain

Examples

trainset <- LETTER[1:15000,]
testset <- LETTER[-(1:15000), -1]
knowns <- trainset[trainset$class==1, -1]
gpdClassifier <- gpdcTrain(train = knowns, k = 10)
predicted <- gpdcTest(train = knowns, test = testset, pre = gpdClassifier)

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GPD Classifier - training

Description

This function is used to train a GPD classifier. It can be used to perform open set classification based on the generalized Pareto distribution.

Usage

gpdcTrain(train, k)

Arguments

train	a data matrix containing the train data. Class labels should not be included.
k	the number of upper order statistics to be used.

Details

For details on the method and parameters see Vignotto and Engelke (2018).

Value

A list of three elements.

pshapes	the estimated rescaled shape parameters for each point in the training dataset.
balls	the estimated radius for each point in the training dataset.
k	the number of upper order statistics used.

Note

Data are not scaled internally; any preprocessing has to be done externally.

Author(s)

Edoardo Vignotto
[email protected]

References

Vignotto, E., & Engelke, S. (2018). Extreme Value Theory for Open Set Classification-GPD and GEV Classifiers. arXiv preprint arXiv:1808.09902.

See Also

gpdcTest

Examples

trainset <- LETTER[1:15000,]
knowns <- trainset[trainset$class==1, -1]
gpdClassifier <- gpdcTrain(train = knowns, k = 10)

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Database of character image features.

Description

A dataset containing 16 features extracted from 20000 handwritten characters.

Usage

LETTER

Format

A data frame with 20000 rows and 17 variables:

class

class labels

V1

first extracted feature

V2

second extracted feature

V3

third extracted feature

V4

4th extracted feature

V5

5th extracted feature

V6

6th extracted feature

V7

7th extracted feature

V8

8th extracted feature

V9

9th extracted feature

V10

10th extracted feature

V11

11th extracted feature

V12

12th extracted feature

V13

13th extracted feature

V14

14th extracted feature

V15

15th extracted feature

V16

16th extracted feature

Source

https://archive.ics.uci.edu/ml/datasets/letter+recognition/

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