weka.classifiers.bayes.net.search.global

Class TabuSearch

java.lang.Object

weka.classifiers.bayes.net.search.SearchAlgorithm

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

weka.classifiers.bayes.net.search.global.HillClimber

weka.classifiers.bayes.net.search.global.TabuSearch

All Implemented Interfaces:

java.io.Serializable, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class TabuSearch
extends HillClimber
implements TechnicalInformationHandler

This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure. Tabu search is hill climbing till an optimum is reached. The following step is the least worst possible step. The last X steps are kept in a list and none of the steps in this so called tabu list is considered in taking the next step. The best network found in this traversal is returned.

For more information see:

R.R. Bouckaert (1995). Bayesian Belief Networks: from Construction to Inference. Utrecht, Netherlands.

BibTeX:

 @phdthesis{Bouckaert1995,
    address = {Utrecht, Netherlands},
    author = {R.R. Bouckaert},
    institution = {University of Utrecht},
    title = {Bayesian Belief Networks: from Construction to Inference},
    year = {1995}
 }
 

Valid options are:

 -L <integer>
  Tabu list length
 

 -U <integer>
  Number of runs
 

 -P <nr of parents>
  Maximum number of parents
 

 -R
  Use arc reversal operation.
  (default false)
 

 -P <nr of parents>
  Maximum number of parents
 

 -R
  Use arc reversal operation.
  (default false)
 

 -N
  Initial structure is empty (instead of Naive Bayes)
 

 -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 (rrb@xm.co.nz)

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
TabuSearch()

Method Summary

Modifier and Type	Method and Description
java.lang.String[]	getOptions() Gets the current settings of the search algorithm.
java.lang.String	getRevision() Returns the revision string.
int	getRuns()
int	getTabuList()
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.
java.lang.String	runsTipText()
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setRuns(int nRuns) Sets the number of runs
void	setTabuList(int nTabuList) Sets the Tabu List length.
java.lang.String	tabuListTipText()

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

getInitAsNaiveBayes, getMaxNrOfParents, getUseArcReversal, setInitAsNaiveBayes, setMaxNrOfParents, setUseArcReversal, useArcReversalTipText

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

buildStructure, 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

TabuSearch

public TabuSearch()

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

getRuns

public int getRuns()

Returns:

number of runs

setRuns

public void setRuns(int nRuns)

Sets the number of runs

Parameters:

nRuns - The number of runs to set

getTabuList

public int getTabuList()

Returns:

the Tabu List length

setTabuList

public void setTabuList(int nTabuList)

Sets the Tabu List length.

Parameters:

nTabuList - The nTabuList to set

listOptions

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

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class HillClimber

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:

 -L <integer>
  Tabu list length
 

 -U <integer>
  Number of runs
 

 -P <nr of parents>
  Maximum number of parents
 

 -R
  Use arc reversal operation.
  (default false)
 

 -P <nr of parents>
  Maximum number of parents
 

 -R
  Use arc reversal operation.
  (default false)
 

 -N
  Initial structure is empty (instead of Naive Bayes)
 

 -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 HillClimber

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 search algorithm.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class HillClimber

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 HillClimber

Returns:

The string.

runsTipText

public java.lang.String runsTipText()

Returns:

a string to describe the Runs option.

tabuListTipText

public java.lang.String tabuListTipText()

Returns:

a string to describe the TabuList option.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class HillClimber

Returns:

the revision