Package weka.classifiers.bayes
Class AODE
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.bayes.AODE
- All Implemented Interfaces:
Serializable
,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:
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: 5516 $
- Author:
- Janice Boughton (jrbought@csse.monash.edu.au), Zhihai Wang (zhw@csse.monash.edu.au)
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances instances) Generates the classifier.double[]
distributionForInstance
(Instance instance) Calculates the class membership probabilities for the given test instance.Returns the tip text for this propertyReturns default capabilities of the classifier.int
Gets the frequency limit.String[]
Gets the current settings of the classifier.Returns the revision string.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
Gets if m-estimaces is being used.int
Gets the weight used in m-estimateReturns a string describing this classifierReturns an enumeration describing the available optionsstatic void
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 limitvoid
setOptions
(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-estimatetoString()
Returns a description of the classifier.void
updateClassifier
(Instance instance) Updates the classifier with the given instance.Returns the tip text for this propertyReturns the tip text for this propertyMethods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Constructor Details
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AODE
public AODE()
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Method Details
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globalInfo
Returns a string describing this classifier- Returns:
- a description of the classifier suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
-
buildClassifier
Generates the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
instances
- set of instances serving as training data- Throws:
Exception
- if the classifier has not been generated successfully
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updateClassifier
Updates the classifier with the given instance.- Specified by:
updateClassifier
in interfaceUpdateableClassifier
- Parameters:
instance
- the new training instance to include in the model
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distributionForInstance
Calculates the class membership probabilities for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- predicted class probability distribution
- Throws:
Exception
- if there is a problem generating the prediction
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NBconditionalProb
Calculates the probability of the specified class for the given test instance, using naive Bayes.- Parameters:
instance
- the instance to be classifiedclassVal
- the class for which to calculate the probability- Returns:
- predicted class probability
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listOptions
Returns an enumeration describing the available options- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options
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setOptions
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 interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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getOptions
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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weightTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setWeight
public void setWeight(int w) Sets the weight for m-estimate- Parameters:
w
- the weight
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getWeight
public int getWeight()Gets the weight used in m-estimate- Returns:
- the frequency limit
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useMEstimatesTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getUseMEstimates
public boolean getUseMEstimates()Gets if m-estimaces is being used.- Returns:
- Value of m_MEstimates.
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setUseMEstimates
public void setUseMEstimates(boolean value) Sets if m-estimates is to be used.- Parameters:
value
- Value to assign to m_MEstimates.
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frequencyLimitTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setFrequencyLimit
public void setFrequencyLimit(int f) Sets the frequency limit- Parameters:
f
- the frequency limit
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getFrequencyLimit
public int getFrequencyLimit()Gets the frequency limit.- Returns:
- the frequency limit
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toString
Returns a description of the classifier. -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- the options
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