weka.classifiers.bayes
Class NaiveBayesUpdateable

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.bayes.NaiveBayes
          weka.classifiers.bayes.NaiveBayesUpdateable

All Implemented Interfaces:

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

public class NaiveBayesUpdateable

extends NaiveBayes

implements UpdateableClassifier

Class for a Naive Bayes classifier using estimator classes. This is the updateable version of NaiveBayes. This classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.

For more information on Naive Bayes classifiers, see

George H. John and Pat Langley (1995). Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. pp. 338-345. Morgan Kaufmann, San Mateo.

Valid options are:

-K
Use kernel estimation for modelling numeric attributes rather than a single normal distribution.

Version:

$Revision: 1.4 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.bayes.NaiveBayes

DEFAULT_NUM_PRECISION, m_ClassDistribution, m_Disc, m_Distributions, m_Instances, m_NumClasses, m_UseDiscretization, m_UseKernelEstimator

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

NaiveBayesUpdateable()

Method Summary

java.lang.String	globalInfo() Returns a string describing this classifier
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setUseSupervisedDiscretization(boolean newblah) Set whether supervised discretization is to be used.

Methods inherited from class weka.classifiers.bayes.NaiveBayes

buildClassifier, distributionForInstance, getOptions, getUseKernelEstimator, getUseSupervisedDiscretization, listOptions, setOptions, setUseKernelEstimator, toString, updateClassifier, useKernelEstimatorTipText, useSupervisedDiscretizationTipText

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

Methods inherited from interface weka.classifiers.UpdateableClassifier

updateClassifier

Constructor Detail

NaiveBayesUpdateable

public NaiveBayesUpdateable()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Overrides:

globalInfo in class NaiveBayes

Returns:

a description of the classifier suitable for displaying in the explorer/experimenter gui

setUseSupervisedDiscretization

public void setUseSupervisedDiscretization(boolean newblah)

Set whether supervised discretization is to be used.

Overrides:

setUseSupervisedDiscretization in class NaiveBayes

Parameters:

newblah - true if supervised discretization is to be used.

main

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

Main method for testing this class.

Parameters:

argv - the options