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

1 2 3 4 5 6 7 8 | ```
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
``` |

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