Ridor

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.rules.Ridor

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.WeightedInstancesHandler
```
public class Ridor
extends weka.classifiers.AbstractClassifier
implements weka.core.AdditionalMeasureProducer, weka.core.WeightedInstancesHandler
```
An 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.
There are five inner classes defined in this class.
The first is Ridor_node, which implements one node in the Ridor tree. It's basically composed of a default class and a set of exception rules to the default class.
The second inner class is RidorRule, which implements a single exception rule using REP.
The last three inner classes are only used in RidorRule. They are Antd, NumericAntd and NominalAntd, which all implement a single antecedent in the RidorRule.
The Antd class is an abstract class, which has two subclasses, NumericAntd and NominalAntd, to implement the corresponding abstract functions. These two subclasses implement the functions related to a antecedent with a nominal attribute and a numeric attribute respectively.
Valid options are:
```
 -F <number of folds>
  Set number of folds for IREP
  One fold is used as pruning set.
  (default 3)
 
```
```
 -S <number of shuffles>
  Set number of shuffles to randomize
  the data in order to get better rule.
  (default 10)
 
```
```
 -A
  Set flag of whether use the error rate 
  of all the data to select the default class
  in each step. If not set, the learner will only use the error rate in the pruning data
 
```
```
 -M
   Set flag of whether use the majority class as
  the default class in each step instead of 
  choosing default class based on the error rate
  (if the flag is not set)
 
```
```
 -N <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
 
```
Version:

$Revision: 10375 $

Author:

Xin XU (xx5@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

Ridor()

Constructors
Constructor and Description
`Ridor()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances instances)` Builds a ripple-down manner rule learner.
`double`	`classifyInstance(weka.core.Instance datum)` Classify the test instance with the rule learner
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String`	`foldsTipText()` Returns the tip text for this property
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`int`	`getFolds()`
`boolean`	`getMajorityClass()`
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`double`	`getMinNo()`
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`int`	`getSeed()`
`int`	`getShuffle()`
`boolean`	`getWholeDataErr()`
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options Valid options are:
`static void`	`main(java.lang.String[] args)` Main method.
`java.lang.String`	`majorityClassTipText()` Returns the tip text for this property
`java.lang.String`	`minNoTipText()` Returns the tip text for this property
`java.lang.String`	`seedTipText()` Returns the tip text for this property
`void`	`setFolds(int fold)`
`void`	`setMajorityClass(boolean m)`
`void`	`setMinNo(double m)`
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSeed(int s)`
`void`	`setShuffle(int sh)`
`void`	`setWholeDataErr(boolean a)`
`java.lang.String`	`shuffleTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Prints the all the rules of the rule learner.
`java.lang.String`	`wholeDataErrTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - Ridor
```
public Ridor()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances instances)
                     throws java.lang.Exception
```
    Builds a ripple-down manner rule learner.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    instances - the training data
    
    Throws:
    
    java.lang.Exception - if classifier can't be built successfully
  - classifyInstance
```
public double classifyInstance(weka.core.Instance datum)
```
    Classify the test instance with the rule learner
    
    Specified by:
    
    classifyInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    classifyInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    datum - the instance to be classified
    
    Returns:
    
    the classification
  - listOptions
```
public java.util.Enumeration<weka.core.Option> listOptions()
```
    Returns an enumeration describing the available options Valid options are:
    -F number
    Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)
    -S number
    Set number of shuffles for randomization. (Default: 10)
    -A
    Set flag of whether use the error rate of all the data to select the default class in each step. If not set, the learner will only use the error rate in the pruning data
    -M
    Set flag of whether use the majority class as the default class in each step instead of choosing default class based on the error rate (if the flag is not set)
    -N number
    Set the minimal weights of instances within a split. (Default: 2)
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -F <number of folds>
  Set number of folds for IREP
  One fold is used as pruning set.
  (default 3)
 
```
```
 -S <number of shuffles>
  Set number of shuffles to randomize
  the data in order to get better rule.
  (default 10)
 
```
```
 -A
  Set flag of whether use the error rate 
  of all the data to select the default class
  in each step. If not set, the learner will only use the error rate in the pruning data
 
```
```
 -M
   Set flag of whether use the majority class as
  the default class in each step instead of 
  choosing default class based on the error rate
  (if the flag is not set)
 
```
```
 -N <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
 
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - foldsTipText
```
public java.lang.String foldsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setFolds
```
public void setFolds(int fold)
```
  - getFolds
```
public int getFolds()
```
  - shuffleTipText
```
public java.lang.String shuffleTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setShuffle
```
public void setShuffle(int sh)
```
  - getShuffle
```
public int getShuffle()
```
  - seedTipText
```
public java.lang.String seedTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSeed
```
public void setSeed(int s)
```
  - getSeed
```
public int getSeed()
```
  - wholeDataErrTipText
```
public java.lang.String wholeDataErrTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setWholeDataErr
```
public void setWholeDataErr(boolean a)
```
  - getWholeDataErr
```
public boolean getWholeDataErr()
```
  - majorityClassTipText
```
public java.lang.String majorityClassTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMajorityClass
```
public void setMajorityClass(boolean m)
```
  - getMajorityClass
```
public boolean getMajorityClass()
```
  - minNoTipText
```
public java.lang.String minNoTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinNo
```
public void setMinNo(double m)
```
  - getMinNo
```
public double getMinNo()
```
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> enumerateMeasures()
```
    Returns an enumeration of the additional measure names
    
    Specified by:
    
    enumerateMeasures in interface weka.core.AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure
    
    Specified by:
    
    getMeasure in interface weka.core.AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - toString
```
public java.lang.String toString()
```
    Prints the all the rules of the rule learner.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a textual description of the classifier
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method.
    
    Parameters:
    
    args - the options for the classifier

Class Ridor

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

Ridor

Method Detail

globalInfo

getCapabilities

buildClassifier

classifyInstance

listOptions

setOptions

getOptions

foldsTipText

setFolds

getFolds

shuffleTipText

setShuffle

getShuffle

seedTipText

setSeed

getSeed

wholeDataErrTipText

setWholeDataErr

getWholeDataErr

majorityClassTipText

setMajorityClass

getMajorityClass

minNoTipText

setMinNo

getMinNo

enumerateMeasures

getMeasure

toString

getRevision

main