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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.bayes.AODE
public class AODE
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.
@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)
Constructor Summary | |
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AODE()
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Method Summary | |
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void |
buildClassifier(Instances instances)
Generates the classifier. |
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 |
getFrequencyLimit()
Gets the frequency limit. |
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 |
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(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(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.Classifier |
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classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public AODE()
Method Detail |
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public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class Classifier
Capabilities
public void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier
in class Classifier
instances
- set of instances serving as training data
java.lang.Exception
- if the classifier has not been generated
successfullypublic void updateClassifier(Instance instance)
updateClassifier
in interface UpdateableClassifier
instance
- the new training instance to include in the modelpublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to be classified
java.lang.Exception
- if there is a problem generating the predictionpublic double NBconditionalProb(Instance instance, int classVal)
instance
- the instance to be classifiedclassVal
- the class for which to calculate the probability
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class Classifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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)
setOptions
in interface OptionHandler
setOptions
in class Classifier
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class Classifier
public java.lang.String weightTipText()
public void setWeight(int w)
w
- the weightpublic int getWeight()
public java.lang.String useMEstimatesTipText()
public boolean getUseMEstimates()
public void setUseMEstimates(boolean value)
value
- Value to assign to m_MEstimates.public java.lang.String frequencyLimitTipText()
public void setFrequencyLimit(int f)
f
- the frequency limitpublic int getFrequencyLimit()
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
public static void main(java.lang.String[] argv)
argv
- the options
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