weka.classifiers.bayes
Class NaiveBayesUpdateable
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.bayes.NaiveBayes
weka.classifiers.bayes.NaiveBayesUpdateable
- All Implemented Interfaces:
- java.io.Serializable, java.lang.Cloneable, UpdateableClassifier, CapabilitiesHandler, OptionHandler, TechnicalInformationHandler, 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, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
BibTeX:
@inproceedings{John1995,
address = {San Mateo},
author = {George H. John and Pat Langley},
booktitle = {Eleventh Conference on Uncertainty in Artificial Intelligence},
pages = {338-345},
publisher = {Morgan Kaufmann},
title = {Estimating Continuous Distributions in Bayesian Classifiers},
year = {1995}
}
Valid options are:
-K
Use kernel density estimator rather than normal
distribution for numeric attributes
-D
Use supervised discretization to process numeric attributes
- Version:
- $Revision: 1.9 $
- Author:
- Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
- See Also:
- Serialized Form
Method Summary |
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. |
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, getCapabilities, getOptions, getUseKernelEstimator, getUseSupervisedDiscretization, listOptions, setOptions, setUseKernelEstimator, toString, updateClassifier, useKernelEstimatorTipText, useSupervisedDiscretizationTipText |
Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
NaiveBayesUpdateable
public NaiveBayesUpdateable()
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
getTechnicalInformation
public 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.
- Specified by:
getTechnicalInformation
in interface TechnicalInformationHandler
- Overrides:
getTechnicalInformation
in class NaiveBayes
- Returns:
- the technical information about this class
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