weka.classifiers.bayes
Class AODE

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by 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: 1.19 $
Author:
Janice Boughton (jrbought@csse.monash.edu.au), Zhihai Wang (zhw@csse.monash.edu.au)
See Also:
Serialized Form

Constructor Summary
AODE()
           
 
Method Summary
 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
Returns:
the revision

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the options