ORION (version 1.0.3)

dropThreshold: Exclude Cascades Based on Threshold

Description

Filters out all cascades that match the comparison with a minimal classwise sensitivity threshold.

Usage

dropThreshold(subcascades = NULL, comparison = ">=", thresh = 0)

Arguments

subcascades

A Subcascades object as it is returned by subcascades-function. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.

comparison

Defines the comparison type (<,>,<=,>=) for the threshold.

thresh

A numeric value between 0 and 1. The minimal sensitivity threshold used to filter the returned cascades. Only cascades that pass this threshold are returned. If 0 is used the returned cascades are filtered for >0 and otherwise >= thresh. For low thresholds the calculation lasts longer.

Value

A Subcascades object comprising the evaluated cascades and their performances. The Subcascades object is made up of a list of matrices. Each matrix comprises the evaluation results of cascades of a specific length and is sorted row-wise according to the achieved minimal classwise sensitivities of the cascades (decreasing). The rownames show the class order by a character string of type '1>2>3' and the entries the sensitivity for each position of the cascade.

See Also

dropSize, keepSize, dropSets, keepSets, keepThreshold

Examples

# NOT RUN {
library(TunePareto)
data(esl)
data = esl$data
labels = esl$labels
foldList = generateCVRuns(labels  = labels,
                          ntimes      = 2,
                          nfold       = 2,
                          leaveOneOut = FALSE,
                          stratified  = TRUE)
predMap = predictionMap(data, labels, foldList = foldList, 
                       classifier = tunePareto.svm(), kernel='linear')
# generate Subcascades object
subc = subcascades(predMap,thresh=0.5)

# filters for cascades that 
# 1. have a minimal classwise sensitivity >= 0.6
dropThreshold(subc,thresh=0.6)
# 2. have a minimal classwise sensitivity <= 0.6
dropThreshold(subc, comparison = '<=', thresh=0.6)
# }