AODEsr

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## weka.classifiers.bayes
Class AODEsr

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.bayes.AODEsr

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, UpdateableClassifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

    public class AODEsr

extends Classifier
implements OptionHandler, WeightedInstancesHandler, UpdateableClassifier, TechnicalInformationHandler

AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see:
Fei Zheng, Geoffrey I. Webb: Efficient Lazy Elimination for Averaged-One Dependence Estimators. In: Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006), 1113-1120, 2006.

BibTeX:

     @inproceedings{Zheng2006,
        author = {Fei Zheng and Geoffrey I. Webb},
        booktitle = {Proceedings of the Twenty-third International Conference on Machine  Learning (ICML 2006)},
        pages = {1113-1120},
        publisher = {ACM Press},
        title = {Efficient Lazy Elimination for Averaged-One Dependence Estimators},
        year = {2006},
        ISBN = {1-59593-383-2}
     }

Valid options are:

     -D
      Output debugging information

     -C
      Impose a critcal value for specialization-generalization relationship
      (default is 50)

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

     -L
      Using Laplace estimation
      (default is m-esimation (m=1))

     -M
      Weight value for m-estimation
      (default is 1.0)

Version:
  $Revision: 1.4 $
Author:
  Fei Zheng, Janice Boughton
See Also:
  Serialized Form

Constructor Summary
`AODEsr()`

Method Summary
`void` | `buildClassifier(Instances instances)` Generates the classifier.
`java.lang.String` | `criticalValueTipText()` Returns the tip text for this property
`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` | `getCriticalValue()` Gets the critical value.
`int` | `getFrequencyLimit()` Gets the frequency limit.
`double` | `getMestWeight()` Gets the weight used in m-estimate
`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` | `getUseLaplace()` Gets if laplace correction is being used.
`java.lang.String` | `globalInfo()` Returns a string describing this classifier
`double` | `LaplaceEstimate(double frequency, double total, double numValues)` Returns the probability estimate, using laplace correction
`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` | `MEstimate(double frequency, double total, double numValues)` Returns the probability estimate, using m-estimate
`java.lang.String` | `mestWeightTipText()` Returns the tip text for this property
`double` | `NBconditionalProb(Instance instance, int classVal)` Calculates the probability of the specified class for the given test instance, using naive Bayes.
`void` | `setCriticalValue(int c)` Sets the critical value
`void` | `setFrequencyLimit(int f)` Sets the frequency limit
`void` | `setMestWeight(double w)` Sets the weight for m-estimate
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setUseLaplace(boolean value)` Sets if laplace correction is to be used.
`java.lang.String` | `toString()` Returns a description of the classifier.
`void` | `updateClassifier(Instance instance)` Updates the classifier with the given instance.
`java.lang.String` | `useLaplaceTipText()` 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

### AODEsr

    public AODEsr()

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
  Throws:
    `java.lang.Exception` - if the instance could not be incorporated 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)
                             throws java.lang.Exception

  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
  Throws:
    `java.lang.Exception` - if there is a problem generating the prediction

### MEstimate

    public double MEstimate(double frequency,
                            double total,
                            double numValues)

  Returns the probability estimate, using m-estimate

  Parameters:
    `frequency` - frequency of value of interest
    `total` - count of all values
    `numValues` - number of different values
  Returns:
    the probability estimate

### LaplaceEstimate

    public double LaplaceEstimate(double frequency,
                                  double total,
                                  double numValues)

  Returns the probability estimate, using laplace correction

  Parameters:
    `frequency` - frequency of value of interest
    `total` - count of all values
    `numValues` - number of different values
  Returns:
    the probability estimate

### 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

       -C
        Impose a critcal value for specialization-generalization relationship
        (default is 50)

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

       -L
        Using Laplace estimation
        (default is m-esimation (m=1))

       -M
        Weight value for m-estimation
        (default is 1.0)

  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

### mestWeightTipText

    public java.lang.String mestWeightTipText()

  Returns the tip text for this property

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

### setMestWeight

    public void setMestWeight(double w)

  Sets the weight for m-estimate

  Parameters:
    `w` - the weight

### getMestWeight

    public double getMestWeight()

  Gets the weight used in m-estimate

  Returns:
    the weight for m-estimation

### useLaplaceTipText

    public java.lang.String useLaplaceTipText()

  Returns the tip text for this property

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

### getUseLaplace

    public boolean getUseLaplace()

  Gets if laplace correction is being used.

  Returns:
    Value of m_Laplace.

### setUseLaplace

    public void setUseLaplace(boolean value)

  Sets if laplace correction is to be used.

  Parameters:
    `value` - Value to assign to m_Laplace.

### 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

### criticalValueTipText

    public java.lang.String criticalValueTipText()

  Returns the tip text for this property

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

### setCriticalValue

    public void setCriticalValue(int c)

  Sets the critical value

  Parameters:
    `c` - the critical value

### getCriticalValue

    public int getCriticalValue()

  Gets the critical value.

  Returns:
    the critical value

### 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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