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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).

Options

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.

Capabilities

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

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