Class AODE

java.lang.Object
weka.classifiers.Classifier
weka.classifiers.bayes.AODE
All Implemented Interfaces:
Serializable, Cloneable, UpdateableClassifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

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:
  • Constructor Details

    • AODE

      public AODE()
  • Method Details

    • globalInfo

      public 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:
    • buildClassifier

      public void buildClassifier(Instances instances) throws Exception
      Generates the classifier.
      Specified by:
      buildClassifier in class Classifier
      Parameters:
      instances - set of instances serving as training data
      Throws:
      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 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:
      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 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(String[] options) throws 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:
      Exception - if an option is not supported
    • getOptions

      public 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 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 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 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 String toString()
      Returns a description of the classifier.
      Overrides:
      toString in class Object
      Returns:
      a description of the classifier as a string.
    • getRevision

      public 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(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - the options