Package
weka.classifiers.rules
Synopsis
The implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions.The exceptions are a set of rules that predict classes other than the default. IREP is used to generate the exceptions.
For more information about Ripple-Down Rules, see:
Brian R. Gaines, Paul Compton (1995). Induction of Ripple-Down Rules Applied to Modeling Large Databases. J. Intell. Inf. Syst.. 5(3):211-228.
Options
The table below describes the options available for Ridor.
Option | Description |
|---|---|
debug | If set to true, classifier may output additional info to the console. |
folds | Determines the amount of data used for pruning. One fold is used for pruning, the rest for growing the rules. |
majorityClass | Whether the majority class is used as default. |
minNo | The minimum total weight of the instances in a rule. |
seed | The seed used for randomizing the data. |
shuffle | Determines how often the data is shuffled before a rule is chosen. If > 1, a rule is learned multiple times and the most accurate rule is chosen. |
wholeDataErr | Whether worth of rule is computed based on all the data or just based on data covered by rule. |
Capabilities
The table below describes the capabilites of Ridor.
Capability | Supported |
|---|---|
Class | Nominal class, Missing class values, Binary class |
Attributes | Empty nominal attributes, Date attributes, Binary attributes, Numeric attributes, Unary attributes, Nominal attributes, Missing values |
Min # of instances | 1 |