weka.classifiers.rules
Class FURIA

java.lang.Object
  extended by weka.classifiers.AbstractClassifier
      extended by weka.classifiers.rules.FURIA
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class FURIA
extends AbstractClassifier
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler

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..

BibTeX:

 @article{Huehn2009,
    author = {Jens Christian Huehn and Eyke Huellermeier},
    journal = {Data Mining and Knowledge Discovery},
    title = {FURIA: An Algorithm for Unordered Fuzzy Rule Induction},
    year = {2009}
 }
 

Valid options are:

 -F <number of folds>
  Set number of folds for REP
  One fold is used as pruning set.
  (default 3)
 -N <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
 -O <number of runs>
  Set the number of runs of
  optimizations. (Default: 2)
 -D
  Set whether turn on the
  debug mode (Default: false)
 -S <seed>
  The seed of randomization
  (Default: 1)
 -E
  Whether NOT check the error rate>=0.5
  in stopping criteria  (default: check)
 -s
  The action performed for uncovered instances.
  (default: use stretching)
 -p
  The T-norm used as fuzzy AND-operator.
  (default: Product T-norm)

Version:
$Revision: 5964 $
Author:
Jens Christian Hühn (huehn@gmx.net), Xin Xu (xx5@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
Serialized Form

Nested Class Summary
 class FURIA.NumericAntd
          The antecedent with numeric attribute
 class FURIA.RipperRule
          This class implements a single rule that predicts specified class.
 
Constructor Summary
FURIA()
           
 
Method Summary
 void buildClassifier(Instances instances)
          Builds the FURIA rule-based model
 java.lang.String checkErrorRateTipText()
          Returns the tip text for this property
 java.lang.String debugTipText()
          Returns the tip text for this property
 double[] distributionForInstance(Instance datum)
          Classify the test instance with the rule learner and provide the class distributions
 java.util.Enumeration enumerateMeasures()
          Returns an enumeration of the additional measure names
 java.lang.String foldsTipText()
          Returns the tip text for this property
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 boolean getCheckErrorRate()
          Gets whether to check for error rate is in stopping criterion
 boolean getDebug()
          Gets whether debug information is output to the console
 int getFolds()
          Gets the number of folds
 double getMeasure(java.lang.String additionalMeasureName)
          Returns the value of the named measure
 double getMinNo()
          Gets the minimum total weight of the instances in a rule
 int getOptimizations()
          Gets the the number of optimization runs
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 java.lang.String getRevision()
          Returns the revision string.
 FastVector getRuleset()
          Get the ruleset generated by FURIA
 RuleStats getRuleStats(int pos)
          Get the statistics of the ruleset in the given position
 long getSeed()
          Gets the current seed value to use in randomizing the data
 TechnicalInformation getTechnicalInformation()
          Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
 SelectedTag getTNorm()
          Gets the TNorm used.
 SelectedTag getUncovAction()
          Gets the action that is performed for uncovered instances.
 java.lang.String globalInfo()
          Returns a string describing classifier
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options Valid options are:
static void main(java.lang.String[] args)
          Main method.
 java.lang.String minNoTipText()
          Returns the tip text for this property
 java.lang.String optimizationsTipText()
          Returns the tip text for this property
 java.lang.String seedTipText()
          Returns the tip text for this property
 void setCheckErrorRate(boolean d)
          Sets whether to check for error rate is in stopping criterion
 void setDebug(boolean d)
          Sets whether debug information is output to the console
 void setFolds(int fold)
          Sets the number of folds to use
 void setMinNo(double m)
          Sets the minimum total weight of the instances in a rule
 void setOptimizations(int run)
          Sets the number of optimization runs
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setSeed(long s)
          Sets the seed value to use in randomizing the data
 void setTNorm(SelectedTag newTNorm)
          Sets the TNorm used.
 void setUncovAction(SelectedTag newUncovAction)
          Sets the action that is performed for uncovered instances.
 java.lang.String TNormTipText()
          Returns the tip text for this property
 java.lang.String toString()
          Prints the all the rules of the rule learner.
 java.lang.String uncovActionTipText()
          Returns the tip text for this property
 
Methods inherited from class weka.classifiers.AbstractClassifier
classifyInstance, forName, makeCopies, makeCopy
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

FURIA

public FURIA()
Method Detail

globalInfo

public java.lang.String globalInfo()
Returns a string describing classifier

Returns:
a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:
getTechnicalInformation in interface TechnicalInformationHandler
Returns:
the technical information about this class

listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options Valid options are:

-F number
The number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

-N number
The minimal weights of instances within a split. (Default: 2)

-O number
Set the number of runs of optimizations. (Default: 2)

-D
Whether turn on the debug mode -S number
The seed of randomization used in FURIA.(Default: 1)

-E
Whether NOT check the error rate >= 0.5 in stopping criteria. (default: check)

-s
The action performed for uncovered instances. (default: use rule stretching)

-p
The T-Norm used as fuzzy AND-operator. (default: Product T-Norm)

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class 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 REP
  One fold is used as pruning set.
  (default 3)
 -N <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
 -O <number of runs>
  Set the number of runs of
  optimizations. (Default: 2)
 -D
  Set whether turn on the
  debug mode (Default: false)
 -S <seed>
  The seed of randomization
  (Default: 1)
 -E
  Whether NOT check the error rate>=0.5
  in stopping criteria  (default: check)
 -s
  The action performed for uncovered instances.
  (default: use stretching)
 -p
  The T-norm used as fuzzy AND-operator.
  (default: Product T-norm)

Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class 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 OptionHandler
Overrides:
getOptions in class AbstractClassifier
Returns:
an array of strings suitable for passing to setOptions

enumerateMeasures

public java.util.Enumeration enumerateMeasures()
Returns an enumeration of the additional measure names

Specified by:
enumerateMeasures in interface 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 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

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)
Sets the number of folds to use

Parameters:
fold - the number of folds

getFolds

public int getFolds()
Gets the number of folds

Returns:
the number of folds

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)
Sets the minimum total weight of the instances in a rule

Parameters:
m - the minimum total weight of the instances in a rule

getMinNo

public double getMinNo()
Gets the minimum total weight of the instances in a rule

Returns:
the minimum total weight of the instances in a rule

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(long s)
Sets the seed value to use in randomizing the data

Parameters:
s - the new seed value

getSeed

public long getSeed()
Gets the current seed value to use in randomizing the data

Returns:
the seed value

optimizationsTipText

public java.lang.String optimizationsTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

setOptimizations

public void setOptimizations(int run)
Sets the number of optimization runs

Parameters:
run - the number of optimization runs

getOptimizations

public int getOptimizations()
Gets the the number of optimization runs

Returns:
the number of optimization runs

debugTipText

public java.lang.String debugTipText()
Returns the tip text for this property

Overrides:
debugTipText in class AbstractClassifier
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

setDebug

public void setDebug(boolean d)
Sets whether debug information is output to the console

Overrides:
setDebug in class AbstractClassifier
Parameters:
d - whether debug information is output to the console

getDebug

public boolean getDebug()
Gets whether debug information is output to the console

Overrides:
getDebug in class AbstractClassifier
Returns:
whether debug information is output to the console

checkErrorRateTipText

public java.lang.String checkErrorRateTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

setCheckErrorRate

public void setCheckErrorRate(boolean d)
Sets whether to check for error rate is in stopping criterion

Parameters:
d - whether to check for error rate is in stopping criterion

getCheckErrorRate

public boolean getCheckErrorRate()
Gets whether to check for error rate is in stopping criterion

Returns:
true if checking for error rate is in stopping criterion

uncovActionTipText

public java.lang.String uncovActionTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getUncovAction

public SelectedTag getUncovAction()
Gets the action that is performed for uncovered instances. It can be UNCOVACTION_STRETCH, UNCOVACTION_APRIORI or UNCOVACTION_REJECT.

Returns:
the current TNorm.

setUncovAction

public void setUncovAction(SelectedTag newUncovAction)
Sets the action that is performed for uncovered instances. It can be UNCOVACTION_STRETCH, UNCOVACTION_APRIORI or UNCOVACTION_REJECT.

Parameters:
newUncovAction - the new action.

TNormTipText

public java.lang.String TNormTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui

getTNorm

public SelectedTag getTNorm()
Gets the TNorm used. Will be either TNORM_PROD or TNORM_MIN.

Returns:
the current TNorm.

setTNorm

public void setTNorm(SelectedTag newTNorm)
Sets the TNorm used. Will be either TNORM_PROD or TNORM_MIN.

Parameters:
newTNorm - the new TNorm.

getRuleset

public FastVector getRuleset()
Get the ruleset generated by FURIA

Returns:
the ruleset

getRuleStats

public RuleStats getRuleStats(int pos)
Get the statistics of the ruleset in the given position

Parameters:
pos - the position of the stats, assuming correct
Returns:
the statistics of the ruleset in the given position

getCapabilities

public Capabilities getCapabilities()
Returns default capabilities of the classifier.

Specified by:
getCapabilities in interface Classifier
Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class AbstractClassifier
Returns:
the capabilities of this classifier
See Also:
Capabilities

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception
Builds the FURIA rule-based model

Specified by:
buildClassifier in interface Classifier
Parameters:
instances - the training data
Throws:
java.lang.Exception - if classifier can't be built successfully

distributionForInstance

public double[] distributionForInstance(Instance datum)
                                 throws java.lang.Exception
Classify the test instance with the rule learner and provide the class distributions

Specified by:
distributionForInstance in interface Classifier
Overrides:
distributionForInstance in class AbstractClassifier
Parameters:
datum - the instance to be classified
Returns:
the distribution
Throws:
java.lang.Exception

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

main

public static void main(java.lang.String[] args)
                 throws java.lang.Exception
Main method.

Parameters:
args - the options for the classifier
Throws:
java.lang.Exception

getRevision

public java.lang.String getRevision()
Description copied from class: AbstractClassifier
Returns the revision string.

Specified by:
getRevision in interface RevisionHandler
Overrides:
getRevision in class AbstractClassifier
Returns:
the revision