weka.classifiers.bayes.net.search.global

Class TAN

java.lang.Object

weka.classifiers.bayes.net.search.SearchAlgorithm

weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm

weka.classifiers.bayes.net.search.global.TAN

All Implemented Interfaces:

java.io.Serializable, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class TAN
extends GlobalScoreSearchAlgorithm
implements TechnicalInformationHandler

This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.

For more information see:

N. Friedman, D. Geiger, M. Goldszmidt (1997). Bayesian network classifiers. Machine Learning. 29(2-3):131-163.

BibTeX:

 @article{Friedman1997,
    author = {N. Friedman and D. Geiger and M. Goldszmidt},
    journal = {Machine Learning},
    number = {2-3},
    pages = {131-163},
    title = {Bayesian network classifiers},
    volume = {29},
    year = {1997}
 }
 

Valid options are:

 -mbc
  Applies a Markov Blanket correction to the network structure, 
  after a network structure is learned. This ensures that all 
  nodes in the network are part of the Markov blanket of the 
  classifier node.
 

 -S [LOO-CV|k-Fold-CV|Cumulative-CV]
  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
 

 -Q
  Use probabilistic or 0/1 scoring.
  (default probabilistic scoring)
 

Version:

$Revision: 10154 $

Author:

Remco Bouckaert

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm

TAGS_CV_TYPE

Constructor Summary

Constructors

Constructor and Description
TAN()

Method Summary

Modifier and Type	Method and Description
void	buildStructure(BayesNet bayesNet, Instances instances) buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu
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.
java.lang.String	globalInfo() This will return a string describing the classifier.
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
void	setOptions(java.lang.String[] options) Parses a given list of options.

Methods inherited from class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm

calcScore, calcScoreWithExtraParent, calcScoreWithMissingParent, calcScoreWithReversedParent, cumulativeCV, CVTypeTipText, getCVType, getMarkovBlanketClassifier, getUseProb, kFoldCV, leaveOneOutCV, markovBlanketClassifierTipText, setCVType, setMarkovBlanketClassifier, setUseProb, useProbTipText

Methods inherited from class weka.classifiers.bayes.net.search.SearchAlgorithm

initAsNaiveBayesTipText, maxNrOfParentsTipText, toString

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

TAN

public TAN()

Method Detail

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

Returns:

the technical information about this class

buildStructure

public void buildStructure(BayesNet bayesNet,
                           Instances instances)
                    throws java.lang.Exception

buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu

Overrides:

buildStructure in class SearchAlgorithm

Parameters:

bayesNet -

instances -

Throws:

java.lang.Exception - if something goes wrong

listOptions

public java.util.Enumeration<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class GlobalScoreSearchAlgorithm

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Valid options are:

 -mbc
  Applies a Markov Blanket correction to the network structure, 
  after a network structure is learned. This ensures that all 
  nodes in the network are part of the Markov blanket of the 
  classifier node.
 

 -S [LOO-CV|k-Fold-CV|Cumulative-CV]
  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
 

 -Q
  Use probabilistic or 0/1 scoring.
  (default probabilistic scoring)
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class GlobalScoreSearchAlgorithm

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class GlobalScoreSearchAlgorithm

Returns:

an array of strings suitable for passing to setOptions

globalInfo

public java.lang.String globalInfo()

This will return a string describing the classifier.

Overrides:

globalInfo in class GlobalScoreSearchAlgorithm

Returns:

The string.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class GlobalScoreSearchAlgorithm

Returns:

the revision