Package

weka.classifiers.rules

Synopsis

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.

Available via the package management system for Weka >= 3.7.2 (fuzzyUnorderedRuleInduction).

The table below describes the options available for FURIA.

Option	Description
TNorm	Choose the T-Norm that is used as fuzzy AND-operator.
checkErrorRate	Whether check for error rate >= 1/2 is included in stopping criterion.
debug	Whether debug information is output to the console.
folds	Determines the amount of data used for pruning. One fold is used for pruning, the rest for growing the rules.
minNo	The minimum total weight of the instances in a rule.
optimizations	The number of optimization runs.
seed	The seed used for randomizing the data.
uncovAction	Selet the action that is performed for uncovered instances.

The table below describes the capabilites of FURIA.

Capability	Supported
Class	Missing class values, Binary class, Nominal class
Attributes	Numeric attributes, Empty nominal attributes, Missing values, Nominal attributes, Binary attributes, Date attributes, Unary attributes
Min # of instances	3