FURIA
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 |