weka.classifiers.bayes

Class AODEsr

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.AODEsr

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.classifiers.UpdateableClassifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

public class AODEsr
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.WeightedInstancesHandler, weka.classifiers.UpdateableClassifier, weka.core.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:

 @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: 8108 $

Author:

Fei Zheng, Janice Boughton

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
AODEsr()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances instances) Generates the classifier.
java.lang.String	criticalValueTipText() Returns the tip text for this property
double[]	distributionForInstance(weka.core.Instance instance) Calculates the class membership probabilities for the given test instance.
java.lang.String	frequencyLimitTipText() Returns the tip text for this property
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getCriticalValue() Gets the critical value.
int	getFrequencyLimit() Gets the frequency limit.
double	getMestWeight() Gets the weight used in m-estimate
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.lang.String	getRevision() Returns the revision string.
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	getUseLaplace() Gets if laplace correction is being used.
java.lang.String	globalInfo() Returns a string describing this classifier
double	LaplaceEstimate(double frequency, double total, double numValues) Returns the probability estimate, using laplace correction
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
double	MEstimate(double frequency, double total, double numValues) Returns the probability estimate, using m-estimate
java.lang.String	mestWeightTipText() Returns the tip text for this property
double	NBconditionalProb(weka.core.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 value
void	setFrequencyLimit(int f) Sets the frequency limit
void	setMestWeight(double w) Sets the weight for m-estimate
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseLaplace(boolean value) Sets if laplace correction is to be used.
java.lang.String	toString() Returns a description of the classifier.
void	updateClassifier(weka.core.Instance instance) Updates the classifier with the given instance.
java.lang.String	useLaplaceTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

AODEsr

public AODEsr()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public 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.

Specified by:

getTechnicalInformation in interface weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.AbstractClassifier

Returns:

the capabilities of this classifier

buildClassifier

public void buildClassifier(weka.core.Instances instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

instances - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

updateClassifier

public void updateClassifier(weka.core.Instance instance)

Updates the classifier with the given instance.

Specified by:

updateClassifier in interface weka.classifiers.UpdateableClassifier

Parameters:

instance - the new training instance to include in the model

Throws:

java.lang.Exception - if the instance could not be incorporated in the model.

distributionForInstance

public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception

Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if there is a problem generating the prediction

NBconditionalProb

public double NBconditionalProb(weka.core.Instance instance,
                                int classVal)
                         throws java.lang.Exception

Calculates the probability of the specified class for the given test instance, using naive Bayes.

Parameters:

instance - the instance to be classified

classVal - the class for which to calculate the probability

Returns:

predicted class probability

Throws:

java.lang.Exception - if there is a problem generating the prediction

MEstimate

public double MEstimate(double frequency,
                        double total,
                        double numValues)

Returns the probability estimate, using m-estimate

Parameters:

frequency - frequency of value of interest

total - count of all values

numValues - number of different values

Returns:

the probability estimate

LaplaceEstimate

public double LaplaceEstimate(double frequency,
                              double total,
                              double numValues)

Returns the probability estimate, using laplace correction

Parameters:

frequency - frequency of value of interest

total - count of all values

numValues - number of different values

Returns:

the probability estimate

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.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:

 -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 interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.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 weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

mestWeightTipText

public java.lang.String mestWeightTipText()

Returns the tip text for this property

Returns:

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

setMestWeight

public void setMestWeight(double w)

Sets the weight for m-estimate

Parameters:

w - the weight

getMestWeight

public double getMestWeight()

Gets the weight used in m-estimate

Returns:

the weight for m-estimation

useLaplaceTipText

public java.lang.String useLaplaceTipText()

Returns the tip text for this property

Returns:

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

getUseLaplace

public boolean getUseLaplace()

Gets if laplace correction is being used.

Returns:

Value of m_Laplace.

setUseLaplace

public void setUseLaplace(boolean value)

Sets if laplace correction is to be used.

Parameters:

value - Value to assign to m_Laplace.

frequencyLimitTipText

public java.lang.String frequencyLimitTipText()

Returns the tip text for this property

Returns:

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

setFrequencyLimit

public void setFrequencyLimit(int f)

Sets the frequency limit

Parameters:

f - the frequency limit

getFrequencyLimit

public int getFrequencyLimit()

Gets the frequency limit.

Returns:

the frequency limit

criticalValueTipText

public java.lang.String criticalValueTipText()

Returns the tip text for this property

Returns:

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

setCriticalValue

public void setCriticalValue(int c)

Sets the critical value

Parameters:

c - the critical value

getCriticalValue

public int getCriticalValue()

Gets the critical value.

Returns:

the critical value

toString

public java.lang.String toString()

Returns a description of the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a description of the classifier as a string.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options