gpdcTest: GPD Classifier - testing

In evtclass: Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers

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
[e-mail]

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)

evtclass documentation built on May 2, 2019, 4:16 a.m.