weka.classifiers.bayes

Class AODE

java.lang.Object

weka.classifiers.Classifier

weka.classifiers.bayes.AODE

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, UpdateableClassifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class AODE
extends Classifier
implements OptionHandler, WeightedInstancesHandler, UpdateableClassifier, TechnicalInformationHandler

AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks.

For more information, see

G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.

Further papers are available at
http://www.csse.monash.edu.au/~webb/.

Can use an m-estimate for smoothing base probability estimates in place of the Laplace correction (via option -M).
Default frequency limit set to 1.

BibTeX:

 @article{Webb2005,
    author = {G. Webb and J. Boughton and Z. Wang},
    journal = {Machine Learning},
    number = {1},
    pages = {5-24},
    title = {Not So Naive Bayes: Aggregating One-Dependence Estimators},
    volume = {58},
    year = {2005}
 }
 

Valid options are:

 -D
  Output debugging information
 

 -F <int>
  Impose a frequency limit for superParents
  (default is 1)

 -M
  Use m-estimate instead of laplace correction
 

 -W <int>
  Specify a weight to use with m-estimate
  (default is 1)

Version:

$Revision: 5516 $

Author:

Janice Boughton (jrbought@csse.monash.edu.au), Zhihai Wang (zhw@csse.monash.edu.au)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
AODE()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances instances) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
java.lang.String	frequencyLimitTipText() Returns the tip text for this property
Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getFrequencyLimit() Gets the frequency limit.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.lang.String	getRevision() Returns the revision string.
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	getUseMEstimates() Gets if m-estimaces is being used.
int	getWeight() Gets the weight used in m-estimate
java.lang.String	globalInfo() Returns a string describing this classifier
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	NBconditionalProb(Instance instance, int classVal) Calculates the probability of the specified class for the given test instance, using naive Bayes.
void	setFrequencyLimit(int f) Sets the frequency limit
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setUseMEstimates(boolean value) Sets if m-estimates is to be used.
void	setWeight(int w) Sets the weight for m-estimate
java.lang.String	toString() Returns a description of the classifier.
void	updateClassifier(Instance instance) Updates the classifier with the given instance.
java.lang.String	useMEstimatesTipText() Returns the tip text for this property
java.lang.String	weightTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

AODE

public AODE()

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 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 TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class Classifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

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

Generates the classifier.

Specified by:

buildClassifier in class 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(Instance instance)

Updates the classifier with the given instance.

Specified by:

updateClassifier in interface UpdateableClassifier

Parameters:

instance - the new training instance to include in the model

distributionForInstance

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

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class Classifier

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(Instance instance,
                                int classVal)

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

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class Classifier

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
 

 -F <int>
  Impose a frequency limit for superParents
  (default is 1)

 -M
  Use m-estimate instead of laplace correction
 

 -W <int>
  Specify a weight to use with m-estimate
  (default is 1)

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

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 OptionHandler

Overrides:

getOptions in class Classifier

Returns:

an array of strings suitable for passing to setOptions

weightTipText

public java.lang.String weightTipText()

Returns the tip text for this property

Returns:

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

setWeight

public void setWeight(int w)

Sets the weight for m-estimate

Parameters:

w - the weight

getWeight

public int getWeight()

Gets the weight used in m-estimate

Returns:

the frequency limit

useMEstimatesTipText

public java.lang.String useMEstimatesTipText()

Returns the tip text for this property

Returns:

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

getUseMEstimates

public boolean getUseMEstimates()

Gets if m-estimaces is being used.

Returns:

Value of m_MEstimates.

setUseMEstimates

public void setUseMEstimates(boolean value)

Sets if m-estimates is to be used.

Parameters:

value - Value to assign to m_MEstimates.

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

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 RevisionHandler

Overrides:

getRevision in class Classifier

Returns:

the revision

main

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

Main method for testing this class.

Parameters:

argv - the options