Package weka.classifiers.bayes
Class AODEsr
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.bayes.AODEsr
- All Implemented Interfaces:
Serializable
,Cloneable
,UpdateableClassifier
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
public class AODEsr
extends Classifier
implements OptionHandler, WeightedInstancesHandler, UpdateableClassifier, TechnicalInformationHandler
AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see:
Fei Zheng, Geoffrey I. Webb: Efficient Lazy Elimination for Averaged-One Dependence Estimators. In: Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006), 1113-1120, 2006. BibTeX:
For more information, see:
Fei Zheng, Geoffrey I. Webb: Efficient Lazy Elimination for Averaged-One Dependence Estimators. In: Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006), 1113-1120, 2006. BibTeX:
@inproceedings{Zheng2006, author = {Fei Zheng and Geoffrey I. Webb}, booktitle = {Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006)}, pages = {1113-1120}, publisher = {ACM Press}, title = {Efficient Lazy Elimination for Averaged-One Dependence Estimators}, year = {2006}, ISBN = {1-59593-383-2} }Valid options are:
-D Output debugging information
-C Impose a critcal value for specialization-generalization relationship (default is 50)
-F Impose a frequency limit for superParents (default is 1)
-L Using Laplace estimation (default is m-esimation (m=1))
-M Weight value for m-estimation (default is 1.0)
- Version:
- $Revision: 5516 $
- Author:
- Fei Zheng, Janice Boughton
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances instances) Generates the classifier.Returns the tip text for this propertydouble[]
distributionForInstance
(Instance instance) Calculates the class membership probabilities for the given test instance.Returns the tip text for this propertyReturns default capabilities of the classifier.int
Gets the critical value.int
Gets the frequency limit.double
Gets the weight used in m-estimateString[]
Gets the current settings of the classifier.Returns the revision string.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
Gets if laplace correction is being used.Returns a string describing this classifierdouble
LaplaceEstimate
(double frequency, double total, double numValues) Returns the probability estimate, using laplace correctionReturns an enumeration describing the available optionsstatic void
Main method for testing this class.double
MEstimate
(double frequency, double total, double numValues) Returns the probability estimate, using m-estimateReturns the tip text for this propertydouble
NBconditionalProb
(Instance instance, int classVal) Calculates the probability of the specified class for the given test instance, using naive Bayes.void
setCriticalValue
(int c) Sets the critical valuevoid
setFrequencyLimit
(int f) Sets the frequency limitvoid
setMestWeight
(double w) Sets the weight for m-estimatevoid
setOptions
(String[] options) Parses a given list of options.void
setUseLaplace
(boolean value) Sets if laplace correction is to be used.toString()
Returns a description of the classifier.void
updateClassifier
(Instance instance) Updates the classifier with the given instance.Returns the tip text for this propertyMethods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Constructor Details
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AODEsr
public AODEsr()
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Method Details
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globalInfo
Returns a string describing this classifier- Returns:
- a description of the classifier suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
-
buildClassifier
Generates the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
instances
- set of instances serving as training data- Throws:
Exception
- if the classifier has not been generated successfully
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updateClassifier
Updates the classifier with the given instance.- Specified by:
updateClassifier
in interfaceUpdateableClassifier
- Parameters:
instance
- the new training instance to include in the model- Throws:
Exception
- if the instance could not be incorporated in the model.
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distributionForInstance
Calculates the class membership probabilities for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- predicted class probability distribution
- Throws:
Exception
- if there is a problem generating the prediction
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NBconditionalProb
Calculates the probability of the specified class for the given test instance, using naive Bayes.- Parameters:
instance
- the instance to be classifiedclassVal
- the class for which to calculate the probability- Returns:
- predicted class probability
- Throws:
Exception
- if there is a problem generating the prediction
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MEstimate
public double MEstimate(double frequency, double total, double numValues) Returns the probability estimate, using m-estimate- Parameters:
frequency
- frequency of value of interesttotal
- count of all valuesnumValues
- number of different values- Returns:
- the probability estimate
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LaplaceEstimate
public double LaplaceEstimate(double frequency, double total, double numValues) Returns the probability estimate, using laplace correction- Parameters:
frequency
- frequency of value of interesttotal
- count of all valuesnumValues
- number of different values- Returns:
- the probability estimate
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listOptions
Returns an enumeration describing the available options- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options
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setOptions
Parses a given list of options. Valid options are:-D Output debugging information
-C Impose a critcal value for specialization-generalization relationship (default is 50)
-F Impose a frequency limit for superParents (default is 1)
-L Using Laplace estimation (default is m-esimation (m=1))
-M Weight value for m-estimation (default is 1.0)
- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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getOptions
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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mestWeightTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setMestWeight
public void setMestWeight(double w) Sets the weight for m-estimate- Parameters:
w
- the weight
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getMestWeight
public double getMestWeight()Gets the weight used in m-estimate- Returns:
- the weight for m-estimation
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useLaplaceTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getUseLaplace
public boolean getUseLaplace()Gets if laplace correction is being used.- Returns:
- Value of m_Laplace.
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setUseLaplace
public void setUseLaplace(boolean value) Sets if laplace correction is to be used.- Parameters:
value
- Value to assign to m_Laplace.
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frequencyLimitTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setFrequencyLimit
public void setFrequencyLimit(int f) Sets the frequency limit- Parameters:
f
- the frequency limit
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getFrequencyLimit
public int getFrequencyLimit()Gets the frequency limit.- Returns:
- the frequency limit
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criticalValueTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setCriticalValue
public void setCriticalValue(int c) Sets the critical value- Parameters:
c
- the critical value
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getCriticalValue
public int getCriticalValue()Gets the critical value.- Returns:
- the critical value
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toString
Returns a description of the classifier. -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- the options
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