IsolationTrees: Building isolation trees
In IsolationForest: Isolation Forest

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/IsolationTrees.R

Building isolation trees

1	IsolationTrees(x, ntree=10, hlim=as.integer(ceiling(log2(nrow(x)))), rowSamp=F, nRowSamp=nrow(x), nmin=1, rFactor=1, colSamp=F, nColSamp=ncol(x), colWeight=c(rep(1,ncol(x))))

`x`	a data frame of training samples
`ntree`	number of tree to build
`hlim`	height limit
`rowSamp`	logical swith to perform random sub-sampling
`nRowSamp`	sub-sampling size; it must be less than or equal to the training sample size
`nmin`	minimum number of sample to form a leaf
`rFactor`	randomisation factor, range from 0 to 1, 0 for fully deterministic, 1 for fully random
`colSamp`	logical switch to perform random attribute-sampling
`nColSamp`	attribute-sampling size; it must be less than or equal to the number of attributes
`colWeight`	attribute weight that is being used in random attribute sub-sampling

Building random binary trees

a data structure that represent an Isolation Forest model

Fei Tony Liu

Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou
Isolation Forest
IEEE International Conference on Data Mining 2008 (ICDM 08), Pisa, Italy, 2008. http://www.gscit.monash.edu.au/gscitweb/loid.php?loid=905282&mimetype=application/pdf

AnomalyScore

library(IsolationForest)
data(stackloss)
# train a model of Isolation Forest
tr<-IsolationTrees(stackloss, rFactor=0)
#evaluate anomaly score
as<-AnomalyScore(stackloss,tr)
# show anomaly score
as$outF