FURIA: Fuzzy Unordered Rule Induction Algorithm Details please see: Jens Christian Huehn, Eyke Huellermeier (2009)
FURIA: An Algorithm for Unordered Fuzzy Rule Induction.Data Mining and Knowledge Discovery..
(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.
F: Set number of folds for REP One fold is used as pruning set. (default 3)
N: Set the minimal weights of instances within a split. (default 2.0)
O: Set the number of runs of optimizations. (Default: 2)
D: Set whether turn on the debug mode (Default: false)
S: The seed of randomization (Default: 1)
E: Whether NOT check the error rate>=0.5 in stopping criteria (default: check)
s: The action performed for uncovered instances. (default: use stretching)
p: The T-norm used as fuzzy AND-operator. (default: Product T-norm)
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: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies:  min # Instance: 3
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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A zipped version of the software site can be downloaded here.
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