AODE

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## 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: 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

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