Implements the isolation forest method for anomaly detection
The data is expected to have two class values for the class attribute, which is ignored at training time.The distributionForInstance() method returns the anomaly score as the first element in the distribution, the second element is one minus this score.
To evaluate performance of this method for a dataset where anomalies are known, simply code the anomalies using the class attribute: normal cases should correspond to the second value of the class attribute, anomalies to the first one.
For more information, see:
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation Forest.
In: ICDM, 413-422, 2008.
(based on WEKA 3.7)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
I: The number of trees in the forest (default 100).
N: The subsample size for each tree (default 256).
S: Random number seed. (default 1)
D: If set, classifier is run in debug mode and may output additional info to the console
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Numeric attributes, Date attributes, Binary class, Missing class values] Dependencies:  min # Instance: 0
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
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