| java.lang.Object
 weka.classifiers.Classifier
weka.classifiers.bayes.AODE
| AODE | | public class AODE extends Classifier implements OptionHandler,WeightedInstancesHandler,UpdateableClassifier,TechnicalInformationHandler(Code) | |
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)
author:
Janice Boughton (jrbought@csse.monash.edu.au)
author:
Zhihai Wang (zhw@csse.monash.edu.au)
version:
$Revision: 1.17 $ |
| serialVersionUID | | final static long serialVersionUID(Code) | | for serialization
|
| NBconditionalProb | | public double NBconditionalProb(Instance instance, int classVal)(Code) | | Calculates the probability of the specified class for the given test
instance, using naive Bayes.
Parameters:
instance - the instance to be classified
Parameters:
classVal - the class for which to calculate the probability predicted class probability |
| buildClassifier | | public void buildClassifier(Instances instances) throws Exception(Code) | | Generates the classifier.
Parameters:
instances - set of instances serving as training data
throws:
Exception - if the classifier has not been generatedsuccessfully |
| distributionForInstance | | public double[] distributionForInstance(Instance instance) throws Exception(Code) | | Calculates the class membership probabilities for the given test
instance.
Parameters:
instance - the instance to be classified predicted class probability distribution
throws:
Exception - if there is a problem generating the prediction |
| frequencyLimitTipText | | public String frequencyLimitTipText()(Code) | | Returns the tip text for this property
tip text for this property suitable fordisplaying in the explorer/experimenter gui |
| getCapabilities | | public Capabilities getCapabilities()(Code) | | Returns default capabilities of the classifier.
the capabilities of this classifier |
| getFrequencyLimit | | public int getFrequencyLimit()(Code) | | Gets the frequency limit.
the frequency limit |
| getOptions | | public String[] getOptions()(Code) | | Gets the current settings of the classifier.
an array of strings suitable for passing to setOptions |
| getTechnicalInformation | | public TechnicalInformation getTechnicalInformation()(Code) | | 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.
the technical information about this class |
| getUseMEstimates | | public boolean getUseMEstimates()(Code) | | Gets if m-estimaces is being used.
Value of m_MEstimates. |
| getWeight | | public int getWeight()(Code) | | Gets the weight used in m-estimate
the frequency limit |
| globalInfo | | public String globalInfo()(Code) | | Returns a string describing this classifier
a description of the classifier suitable fordisplaying in the explorer/experimenter gui |
| listOptions | | public Enumeration listOptions()(Code) | | Returns an enumeration describing the available options
an enumeration of all the available options |
| main | | public static void main(String[] argv)(Code) | | Main method for testing this class.
Parameters:
argv - the options |
| setFrequencyLimit | | public void setFrequencyLimit(int f)(Code) | | Sets the frequency limit
Parameters:
f - the frequency limit |
| setOptions | | public void setOptions(String[] options) throws Exception(Code) | | 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)
Parameters:
options - the list of options as an array of strings
throws:
Exception - if an option is not supported |
| setUseMEstimates | | public void setUseMEstimates(boolean value)(Code) | | Sets if m-estimates is to be used.
Parameters:
value - Value to assign to m_MEstimates. |
| setWeight | | public void setWeight(int w)(Code) | | Sets the weight for m-estimate
Parameters:
w - the weight |
| toString | | public String toString()(Code) | | Returns a description of the classifier.
a description of the classifier as a string. |
| updateClassifier | | public void updateClassifier(Instance instance)(Code) | | Updates the classifier with the given instance.
Parameters:
instance - the new training instance to include in the model |
| useMEstimatesTipText | | public String useMEstimatesTipText()(Code) | | Returns the tip text for this property
tip text for this property suitable fordisplaying in the explorer/experimenter gui |
| weightTipText | | public String weightTipText()(Code) | | Returns the tip text for this property
tip text for this property suitable fordisplaying in the explorer/experimenter gui |
| Fields inherited from weka.classifiers.Classifier | protected boolean m_Debug(Code)(Java Doc)
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