weka.classifiers.bayes
Class AODE

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.bayes.AODE

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class AODE

extends Classifier

implements OptionHandler, 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 (2003). Not So Naive Bayes. Submitted for publication
G. Webb, J. Boughton & Z. Wang (2002). Averaged One-Dependence Estimators: Preliminary Results. AI2002 Data Mining Workshop, Canberra. Valid options are:

-D
Debugging information is printed if this flag is specified.

-F
Specify the frequency limit for parent attributes.

Version:

$Revision: 1.7 $

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	frequencyLimitForParentAttributesTipText() Returns the tip text for this property
int	getFrequencyLimitForParentAttributes() Return the frequency limit for parent attributes
java.lang.String[]	getOptions() Gets the current settings of the classifier.
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	setFrequencyLimitForParentAttributes(int fl) Set the frequency limit for parent attributes
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns a description of the classifier.

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, 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

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

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

Throws:

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

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
Debugging information is printed.

-F
Specify the frequency limit for parent attributes.

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

frequencyLimitForParentAttributesTipText

public java.lang.String frequencyLimitForParentAttributesTipText()

Returns the tip text for this property

Returns:

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

setFrequencyLimitForParentAttributes

public void setFrequencyLimitForParentAttributes(int fl)

Set the frequency limit for parent attributes

Parameters:

fl - an int value

getFrequencyLimitForParentAttributes

public int getFrequencyLimitForParentAttributes()

Return the frequency limit for parent attributes

Returns:

an int 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.

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options