AODE

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.bayes.AODE

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.classifiers.UpdateableClassifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler
```
public class AODE
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.WeightedInstancesHandler, weka.classifiers.UpdateableClassifier, weka.core.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: 8108 $

Author:

Janice Boughton (jrbought@csse.monash.edu.au), Zhihai Wang (zhw@csse.monash.edu.au)

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

AODE()

Constructors
Constructor and Description
`AODE()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances instances)` Generates the classifier.
`double[]`	`distributionForInstance(weka.core.Instance instance)` Calculates the class membership probabilities for the given test instance.
`java.lang.String`	`frequencyLimitTipText()` Returns the tip text for this property
`weka.core.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.
`weka.core.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(weka.core.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(weka.core.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.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- 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 weka.core.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 weka.core.TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances instances)
                     throws java.lang.Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.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(weka.core.Instance instance)
```
    Updates the classifier with the given instance.
    
    Specified by:
    
    updateClassifier in interface weka.classifiers.UpdateableClassifier
    
    Parameters:
    
    instance - the new training instance to include in the model
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    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(weka.core.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 weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    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 weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    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 weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.AbstractClassifier
    
    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 weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class AODE

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

AODE

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

updateClassifier

distributionForInstance

NBconditionalProb

listOptions

setOptions

getOptions

weightTipText

setWeight

getWeight

useMEstimatesTipText

getUseMEstimates

setUseMEstimates

frequencyLimitTipText

setFrequencyLimit

getFrequencyLimit

toString

getRevision

main