rfOutliers: Random forest based outlier detection

In CORElearn: Classification, Regression and Feature Evaluation

Random forest based outlier detection

Description

Based on random forest instance proximity measure detects training cases which are different to all other cases.

Usage

rfOutliers(model, dataset)

Arguments

model	a random forest model returned by CoreModel
dataset	a training set used to generate the model

Details

Strangeness is defined using the random forest model via a proximity matrix (see rfProximity). If the number is greater than 10, the case can be considered an outlier according to Breiman 2001.

Value

For each instance from a dataset the function returns a numeric score of its strangeness to other cases.

Author(s)

John Adeyanju Alao (as a part of his BSc thesis) and Marko Robnik-Sikonja (thesis supervisor)

References

Leo Breiman: Random Forests. Machine Learning Journal, 45:5-32, 2001

See Also

CoreModel, rfProximity, rfClustering.

Examples

#first create a random forest tree using CORElearn
dataset <- iris
md <- CoreModel(Species ~ ., dataset, model="rf", rfNoTrees=30, 
                maxThreads=1)
outliers <- rfOutliers(md, dataset)
plot(abs(outliers))
#for a nicer display try 
plot(md, dataset, rfGraphType="outliers")

destroyModels(md) # clean up

CORElearn documentation built on Nov. 18, 2022, 5:08 p.m.