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java.lang.Objectweka.classifiers.bayes.net.search.SearchAlgorithm
weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
weka.classifiers.bayes.net.search.local.TAN
public class TAN
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.
@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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES] Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
Field Summary |
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Fields inherited from class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm |
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TAGS_SCORE_TYPE |
Constructor Summary | |
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TAN()
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Method Summary | |
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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. |
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 |
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.local.LocalScoreSearchAlgorithm |
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calcNodeScore, calcScoreWithExtraParent, calcScoreWithMissingParent, getMarkovBlanketClassifier, getScoreType, logScore, markovBlanketClassifierTipText, scoreTypeTipText, setMarkovBlanketClassifier, setScoreType |
Methods inherited from class weka.classifiers.bayes.net.search.SearchAlgorithm |
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initAsNaiveBayesTipText, maxNrOfParentsTipText, toString |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public TAN()
Method Detail |
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public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public void buildStructure(BayesNet bayesNet, Instances instances) throws java.lang.Exception
buildStructure
in class LocalScoreSearchAlgorithm
bayesNet
- the networkinstances
- the data to use
java.lang.Exception
- if something goes wrongpublic java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class LocalScoreSearchAlgorithm
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES] Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
setOptions
in interface OptionHandler
setOptions
in class LocalScoreSearchAlgorithm
options
- the list of options as an array of strings
java.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class LocalScoreSearchAlgorithm
public java.lang.String globalInfo()
globalInfo
in class LocalScoreSearchAlgorithm
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