Package weka.classifiers.bayes

Class 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 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

      • 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.

      • main

        public static void main​(java.lang.String[] argv)
        Main method for testing this class.
        Parameters:
        argv - the options