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

Field Summary

private double[]	m_ClassCounts The number of times each class value occurs in the dataset
private int	m_ClassIndex The index of the class attribute
private double[][][]	m_CondiCounts 3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts
private boolean	m_Debug If true, outputs debugging info
private int[]	m_Frequencies The frequency of each attribute value for the dataset
private Instances	m_Instances The dataset
private int	m_Limit An att's frequency must be this value or more to be a superParent
private int	m_NumAttributes The number of attributes in dataset, including class
private int[]	m_NumAttValues The number of values for each attribute
private int	m_NumClasses The number of classes
private int	m_NumInstances The number of instances in the dataset
private int[]	m_StartAttIndex The starting index (in the m_CondiCounts matrix) of each attribute
private int[][]	m_SumForCounts The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]
private double	m_SumInstances The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.
private int	m_TotalAttValues The total number of values for all attributes (not including class).

Constructor Summary

AODE()

Method Summary

private void	addToCounts(Instance instance) Puts an instance's values into m_CondiCounts, m_ClassCounts and m_SumInstances.
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

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_CondiCounts

private double[][][] m_CondiCounts

3D array (m_NumClasses * m_TotalAttValues * m_TotalAttValues) of attribute counts

m_ClassCounts

private double[] m_ClassCounts

The number of times each class value occurs in the dataset

m_SumForCounts

private int[][] m_SumForCounts

The sums of attribute-class counts -- if there are no missing values for att, then m_SumForCounts[classVal][att] will be the same as m_ClassCounts[classVal]

m_NumClasses

private int m_NumClasses

The number of classes

m_NumAttributes

private int m_NumAttributes

The number of attributes in dataset, including class

m_NumInstances

private int m_NumInstances

The number of instances in the dataset

m_ClassIndex

private int m_ClassIndex

The index of the class attribute

m_Instances

private Instances m_Instances

The dataset

m_TotalAttValues

private int m_TotalAttValues

The total number of values for all attributes (not including class). Eg. for three atts each with two possible values, m_TotalAttValues would be 6. This variable is used when allocating space for m_CondiCounts matrix.

m_StartAttIndex

private int[] m_StartAttIndex

The starting index (in the m_CondiCounts matrix) of each attribute

m_NumAttValues

private int[] m_NumAttValues

The number of values for each attribute

m_Frequencies

private int[] m_Frequencies

The frequency of each attribute value for the dataset

m_SumInstances

private double m_SumInstances

The number of valid class values observed in dataset -- with no missing classes, this number is the same as m_NumInstances.

m_Limit

private int m_Limit

An att's frequency must be this value or more to be a superParent

m_Debug

private boolean m_Debug

If true, outputs debugging info

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

addToCounts

private void addToCounts(Instance instance)

Puts an instance's values into m_CondiCounts, m_ClassCounts and m_SumInstances.

Parameters:

instance - the instance whose values are to be put into the counts variables

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.

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