|
||||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Objectweka.classifiers.AbstractClassifier
weka.classifiers.rules.FURIA
public class FURIA
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..
@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)
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 |
---|
public FURIA()
Method Detail |
---|
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface 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 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)
listOptions
in interface OptionHandler
listOptions
in class AbstractClassifier
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 The action performed for uncovered instances. (default: use stretching)
-p The T-norm used as fuzzy AND-operator. (default: Product T-norm)
setOptions
in interface OptionHandler
setOptions
in class AbstractClassifier
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 OptionHandler
getOptions
in class AbstractClassifier
public java.util.Enumeration enumerateMeasures()
enumerateMeasures
in interface AdditionalMeasureProducer
public double getMeasure(java.lang.String additionalMeasureName)
getMeasure
in interface 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 AbstractClassifier
public void setDebug(boolean d)
setDebug
in class AbstractClassifier
d
- whether debug information is output to the consolepublic boolean getDebug()
getDebug
in class AbstractClassifier
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 uncovActionTipText()
public SelectedTag getUncovAction()
public void setUncovAction(SelectedTag newUncovAction)
newUncovAction
- the new action.public java.lang.String TNormTipText()
public SelectedTag getTNorm()
public void setTNorm(SelectedTag newTNorm)
newTNorm
- the new TNorm.public FastVector getRuleset()
public RuleStats getRuleStats(int pos)
pos
- the position of the stats, assuming correct
public Capabilities getCapabilities()
getCapabilities
in interface Classifier
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class AbstractClassifier
Capabilities
public void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier
in interface Classifier
instances
- the training data
java.lang.Exception
- if classifier can't be built successfullypublic double[] distributionForInstance(Instance datum) throws java.lang.Exception
distributionForInstance
in interface Classifier
distributionForInstance
in class AbstractClassifier
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()
AbstractClassifier
getRevision
in interface RevisionHandler
getRevision
in class AbstractClassifier
|
||||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |