FURIA

java.lang.Object
- keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier
- - keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, WeightedInstancesHandler
```
public class FURIA
extends Classifier
implements WeightedInstancesHandler
```
This class implements the FURIA algorithm proposed by Hühn and Hüllermeier 2009

The FURIA algorithm is a fuzzy rule learner based on the JRip implementation (RIPRER). The main difference between FURIA and JRip is that FURIA makes no use of default rules. Furthermore FURIA has a changed pruning procedure, which means that the pruning during the IREP* runs was deactivated permanently. It was found out experimentally that this improved the classification rate. The following description from the JRip class was altered to describe the methodology of FURIA:

Initialize RS = {}, and for each of both class DO:

1. Building stage:
Repeat 1.1 until the description length (DL) of the ruleset and examples is 64 bits greater than the smallest DL met so far, or there are no positive examples, or the error rate >= 50%.

1.1. Grow phase:
Grow one rule by greedily adding antecedents (or conditions) to the rule until the rule is perfect (i.e. 100% accurate). The procedure tries every possible value of each attribute and selects the condition with highest information gain: p(log(p/t)-log(P/T)).

2. Optimization stage:
after generating the initial ruleset {Ri}, generate and prune two variants of each rule Ri from randomized data using procedure 1.1 and X.1. But one variant is generated from an empty rule while the other is generated by greedily adding antecedents to the original rule. Moreover, the pruning metric used here is (TP+TN)/(P+N).Then the smallest possible DL for each variant and the original rule is computed. The variant with the minimal DL is selected as the final representative of Ri in the ruleset.After all the rules in {Ri} have been examined and if there are still residual positives, more rules are generated based on the residual positives using Building Stage again.
3. Delete the rules from the ruleset that would increase the DL of the whole ruleset if it were in it. and add resultant ruleset to RS.
ENDDO

Fuzzification of RS:
For each rule r in every ruleset in RS DO
4. Fuzzification of antecedents:
Apply greedy strategy to fuzzify the existing antecedents in r the following way:

4.1 Examine all possible support bounds and select the one which gains the highest purity on the training data.
4.2 Set the maximum support bound determined in 4.1 and restart with 4.1 but withouth the fuzzified antecedent.
ENDDO

X.1. Pruning:
Incrementally prune each rule and allow the pruning of any final sequences of the antecedents;The pruning metric is (p-n)/(p+n) -- but it's actually 2p/(p+n) -1, so in this implementation we simply use p/(p+n) (actually (p+1)/(p+n+2), thus if p+n is 0, it's 0.5).

Classification time:
If an instance is not covered by any rule, apply a rule stretching mechanism: Cut every rule just in front of the first discriminating antecedent such that the this way stretched rule covers the instance. Doing this for all rules will lead to a set of rules in which each one covers the instance (or is empty and may be excluded). To determine the rule that assigns the class calculate the weight given by its purity using the m-measure on the one hand the Laplace-fraction of antecedents left in comparison to the original number of the respective rule. The rule that maximizes that value is from the class that will be assigned.
BibTeX:
```
  @article{huehn2009furia,
    author = {Jens Christian Hühn and Eyke Hüllermeier},
    journal = {Data Mining and Knowledge Discovery},
    title = {FURIA: An Algorithm for Unordered Fuzzy Rule Induction},
    year = {to appear}
 }
 @inproceedings{Cohen1995,
    author = {William W. Cohen},
    booktitle = {Twelfth International Conference on Machine Learning},
    pages = {115-123},
    publisher = {Morgan Kaufmann},
    title = {Fast Effective Rule Induction},
    year = {1995}
 }
 
```
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
  Whether use rule stretching or refrain from classifying
  (default: use stretching)
```
Version:

$Revision: 1.1 $

Author:

written by Jens Christian Hühn (huehn@gmx.net), modified for KEEL by Alberto Fernández (alberto.fernandez@ujaen.es) [University of Jaen] 23/10/2013

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`FILTER_NONE` filter: No normalization/standardization
`static int`	`FILTER_NORMALIZE` filter: Normalize training data
`static int`	`FILTER_STANDARDIZE` filter: Standardize training data
`protected AttributeWeka`	`m_Class` The class attribute of the data
`protected boolean`	`m_Debug` Whether in a debug mode
`protected FastVector`	`m_Distributions` The predicted class distribution
`protected Randomize`	`m_Random` Random object used in this class
`FastVector`	`m_Ruleset` The ruleset
`protected FastVector`	`m_RulesetStats` The RuleStats for the ruleset of each class value
`protected long`	`m_Seed` The seed to perform randomization
`protected double`	`m_Total` # of all the possible conditions in a rule

Constructor Summary

Constructors
Constructor and Description

FURIA(parseParameters params)
Constructor.

Constructors
Constructor and Description
`FURIA(parseParameters params)` Constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances instances)` Builds Ripper in the order of class frequencies.
`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
`void`	`execute()` It launches the FURIA algorithm
`java.lang.String`	`foldsTipText()` Returns the tip text for this property
`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`	`getRevision()` Returns revision ( string "1.0")
`FastVector`	`getRuleset()` Get the ruleset generated by Ripper
`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
`boolean`	`getUseRuleStretching()` Gets whether pruning is performed
`protected Instances`	`InstancesKEEL2Weka(InstanceSet is, int preprocessType)` Creates a new allocated WEKA's set of Instances (i.e.
`java.lang.String`	`minNoTipText()` Returns the tip text for this property
`java.lang.String`	`optimizationsTipText()` Returns the tip text for this property
`protected Instances`	`rulesetForOneClass(double expFPRate, Instances data, double classIndex, double defDL)` Build a ruleset for the given class according to the given data
`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`	`setSeed(long s)` Sets the seed value to use in randomizing the data
`void`	`setUseRuleStretching(boolean d)` Sets whether pruning is performed
`java.lang.String`	`toString()` Prints the all the rules of the rule learner.
`java.lang.String`	`useRuleStretchingTipText()` Returns the tip text for this property

Methods inherited from class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier
classifyInstance, forName, getOptions, listOptions, makeCopies, makeCopy, setOptions

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

- Field Detail
  - m_Class
```
protected AttributeWeka m_Class
```
    The class attribute of the data
  - m_Ruleset
```
public FastVector m_Ruleset
```
    The ruleset
  - m_Distributions
```
protected FastVector m_Distributions
```
    The predicted class distribution
  - m_Random
```
protected Randomize m_Random
```
    Random object used in this class
  - m_Total
```
protected double m_Total
```
    # of all the possible conditions in a rule
  - m_Seed
```
protected long m_Seed
```
    The seed to perform randomization
  - m_Debug
```
protected boolean m_Debug
```
    Whether in a debug mode
  - m_RulesetStats
```
protected FastVector m_RulesetStats
```
    The RuleStats for the ruleset of each class value
  - FILTER_NORMALIZE
```
public static final int FILTER_NORMALIZE
```
    filter: Normalize training data
    
    See Also:
    
    Constant Field Values
  - FILTER_STANDARDIZE
```
public static final int FILTER_STANDARDIZE
```
    filter: Standardize training data
    
    See Also:
    
    Constant Field Values
  - FILTER_NONE
```
public static final int FILTER_NONE
```
    filter: No normalization/standardization
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - FURIA
```
public FURIA(parseParameters params)
```
    Constructor. Build the FURIA object and parse all parameters with the parser given.
    
    Parameters:
    
    params - given parser.
- Method Detail
  - execute
```
public void execute()
```
    It launches the FURIA algorithm
  - InstancesKEEL2Weka
```
protected Instances InstancesKEEL2Weka(InstanceSet is,
                                       int preprocessType)
```
    Creates a new allocated WEKA's set of Instances (i.e. Instances) from a KEEL's set of instances (i.e. InstanceSet).
    
    Parameters:
    
    is - The KEEL Instance set
    
    preprocessType - An integer with the type of preprocess done before exporting data to Weka format (0 = normalize, 1 = standardize, 2 = do nothing).
    
    Returns:
    
    A new allocated WEKA formatted Instance set
  - enumerateMeasures
```
public java.util.Enumeration enumerateMeasures()
```
    Returns an enumeration of the additional measure names
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure
    
    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 Classifier
    
    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 Classifier
    
    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 Classifier
    
    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
  - useRuleStretchingTipText
```
public java.lang.String useRuleStretchingTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseRuleStretching
```
public void setUseRuleStretching(boolean d)
```
    Sets whether pruning is performed
    
    Parameters:
    
    d - Whether pruning is performed
  - getUseRuleStretching
```
public boolean getUseRuleStretching()
```
    Gets whether pruning is performed
    
    Returns:
    
    true if pruning is performed
  - getRuleset
```
public FastVector getRuleset()
```
    Get the ruleset generated by Ripper
    
    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
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws java.lang.Exception
```
    Builds Ripper in the order of class frequencies. For each class it's built in two stages: building and optimization
    
    Specified by:
    
    buildClassifier in class 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
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    datum - the instance to be classified
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception
  - rulesetForOneClass
```
protected Instances rulesetForOneClass(double expFPRate,
                                       Instances data,
                                       double classIndex,
                                       double defDL)
                                throws java.lang.Exception
```
    Build a ruleset for the given class according to the given data
    
    Parameters:
    
    expFPRate - the expected FP/(FP+FN) used in DL calculation
    
    data - the given data
    
    classIndex - the given class index
    
    defDL - the default DL in the data
    
    Returns:
    
    ruleset for the given class according to the given data
    
    Throws:
    
    java.lang.Exception - if the ruleset can be built properly
  - 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 revision ( string "1.0")
    
    Returns:
    
    revision ( string "1.0")

Class FURIA

Field Summary

Constructor Summary

Method Summary

Methods inherited from class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier

Methods inherited from class java.lang.Object

Field Detail

m_Class

m_Ruleset

m_Distributions

m_Random

m_Total

m_Seed

m_Debug

m_RulesetStats

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

Constructor Detail

FURIA

Method Detail

execute

InstancesKEEL2Weka

enumerateMeasures

getMeasure

foldsTipText

setFolds

getFolds

minNoTipText

setMinNo

getMinNo

seedTipText

setSeed

getSeed

optimizationsTipText

setOptimizations

getOptimizations

debugTipText

setDebug

getDebug

checkErrorRateTipText

setCheckErrorRate

getCheckErrorRate

useRuleStretchingTipText

setUseRuleStretching

getUseRuleStretching

getRuleset

getRuleStats

buildClassifier

distributionForInstance

rulesetForOneClass

toString

getRevision