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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.rules.FURIA
public class FURIA
This class implements the FURIA algorithm proposed by Hühn and Hüllermeier 2008
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.
@article{huehn2008furia, author = {Jens Christian Hühn and Eyke Hüllermeier}, journal = {}, title = {FURIA: Fuzzy Unordered Rule Induction Algorithm}, year = {2008} } @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)
Nested Class Summary | |
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class |
FURIA.NumericAntd
The antecedent with numeric attribute |
class |
FURIA.RipperRule
This class implements a single rule that predicts specified class. |
Field Summary | |
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weka.core.FastVector |
m_Ruleset
The ruleset |
Constructor Summary | |
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FURIA()
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Method Summary | |
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void |
buildClassifier(weka.core.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(weka.core.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 |
weka.core.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. |
weka.core.FastVector |
getRuleset()
Get the ruleset generated by Ripper |
weka.classifiers.rules.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 |
weka.core.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. |
boolean |
getUseRuleStretching()
Gets whether pruning is performed |
java.lang.String |
globalInfo()
Returns a string describing classifier |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options Valid options are: -F number The number of folds for reduced error pruning. |
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 |
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 weka.classifiers.Classifier |
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classifyInstance, forName, makeCopies, makeCopy |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Field Detail |
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public weka.core.FastVector m_Ruleset
Constructor Detail |
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public FURIA()
Method Detail |
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public java.lang.String globalInfo()
public weka.core.TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface weka.core.TechnicalInformationHandler
public java.util.Enumeration listOptions()
-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 Ripper.(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)
listOptions
in interface weka.core.OptionHandler
listOptions
in class weka.classifiers.Classifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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)
setOptions
in interface weka.core.OptionHandler
setOptions
in class weka.classifiers.Classifier
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface weka.core.OptionHandler
getOptions
in class weka.classifiers.Classifier
public java.util.Enumeration enumerateMeasures()
enumerateMeasures
in interface weka.core.AdditionalMeasureProducer
public double getMeasure(java.lang.String additionalMeasureName)
getMeasure
in interface weka.core.AdditionalMeasureProducer
additionalMeasureName
- the name of the measure to query for its value
java.lang.IllegalArgumentException
- if the named measure is not supportedpublic java.lang.String foldsTipText()
public void setFolds(int fold)
fold
- the number of foldspublic int getFolds()
public java.lang.String minNoTipText()
public void setMinNo(double m)
m
- the minimum total weight of the instances in a rulepublic double getMinNo()
public java.lang.String seedTipText()
public void setSeed(long s)
s
- the new seed valuepublic long getSeed()
public java.lang.String optimizationsTipText()
public void setOptimizations(int run)
run
- the number of optimization runspublic int getOptimizations()
public java.lang.String debugTipText()
debugTipText
in class weka.classifiers.Classifier
public void setDebug(boolean d)
setDebug
in class weka.classifiers.Classifier
d
- whether debug information is output to the consolepublic boolean getDebug()
getDebug
in class weka.classifiers.Classifier
public java.lang.String checkErrorRateTipText()
public void setCheckErrorRate(boolean d)
d
- whether to check for error rate is in stopping criterionpublic boolean getCheckErrorRate()
public java.lang.String useRuleStretchingTipText()
public void setUseRuleStretching(boolean d)
d
- Whether pruning is performedpublic boolean getUseRuleStretching()
public weka.core.FastVector getRuleset()
public weka.classifiers.rules.RuleStats getRuleStats(int pos)
pos
- the position of the stats, assuming correct
public weka.core.Capabilities getCapabilities()
getCapabilities
in interface weka.core.CapabilitiesHandler
getCapabilities
in class weka.classifiers.Classifier
Capabilities
public void buildClassifier(weka.core.Instances instances) throws java.lang.Exception
buildClassifier
in class weka.classifiers.Classifier
instances
- the training data
java.lang.Exception
- if classifier can't be built successfullypublic double[] distributionForInstance(weka.core.Instance datum) throws java.lang.Exception
distributionForInstance
in class weka.classifiers.Classifier
datum
- the instance to be classified
java.lang.Exception
public java.lang.String toString()
toString
in class java.lang.Object
public static void main(java.lang.String[] args) throws java.lang.Exception
args
- the options for the classifier
java.lang.Exception
public java.lang.String getRevision()
weka.core.RevisionHandler
getRevision
in interface weka.core.RevisionHandler
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