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 |