nplSVM: Neyman-Pearson-Learning

In liquidSVM: A Fast and Versatile SVM Package

Description

This routine provides binary classifiers that satisfy a predefined error rate on one type of error and that simlutaneously minimize the other type of error. For convenience some points on the ROC curve around the predefined error rate are returned. nplNPL performs Neyman-Pearson-Learning for classification.

Usage

nplSVM(x, y, ..., class = 1, constraint = 0.05,
  constraint.factors = c(3, 4, 6, 9, 12)/6, do.select = TRUE)

Arguments

x	either a formula or the features
y	either the data or the labels corresponding to the features x. It can be a character in which case the data is loaded using liquidData. If it is of type liquidData then after training and selection the model is tested using the testing data (y$test).
...	configuration parameters, see Configuration. Can be threads=2, display=1, gpus=1, etc.
class	is the normal class (the other class becomes the alarm class)
constraint	gives the false alarm rate which should be achieved
constraint.factors	specifies the factors around constraint
do.select	if TRUE also does the whole selection for this model

Details

Please look at the demo-vignette (vignette('demo')) for more examples. The labels should only have value c(1,-1).

min_weight, max_weight, weight_steps: you might have to define which weighted classification problems will be considered. The choice is usually a bit tricky. Good luck ...

Value

an object of type svm. Depending on the usage this object has also $train_errors, $select_errors, and $last_result properties.

Examples

## Not run: 
model <- nplSVM(Y ~ ., 'banana-bc', display=1)

## a worked example can be seen at
vignette("demo",package="liquidSVM")

## End(Not run)
 

liquidSVM documentation built on Sept. 15, 2019, 1:02 a.m.