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java.lang.Objectweka.classifiers.bayes.net.search.SearchAlgorithm
weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
weka.classifiers.bayes.net.search.global.K2
public class K2
This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.
For more information see:
G.F. Cooper, E. Herskovits (1990). A Bayesian method for constructing Bayesian belief networks from databases.
G. Cooper, E. Herskovits (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 9(4):309-347.
Works with nominal variables and no missing values only.
@proceedings{Cooper1990,
author = {G.F. Cooper and E. Herskovits},
booktitle = {Proceedings of the Conference on Uncertainty in AI},
pages = {86-94},
title = {A Bayesian method for constructing Bayesian belief networks from databases},
year = {1990}
}
@article{Cooper1992,
author = {G. Cooper and E. Herskovits},
journal = {Machine Learning},
number = {4},
pages = {309-347},
title = {A Bayesian method for the induction of probabilistic networks from data},
volume = {9},
year = {1992}
}
Valid options are:
-N Initial structure is empty (instead of Naive Bayes)
-P <nr of parents> Maximum number of parents
-R Random order. (default false)
-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)
| Field Summary |
|---|
| Fields inherited from class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm |
|---|
TAGS_CV_TYPE |
| Constructor Summary | |
|---|---|
K2()
|
|
| Method Summary | |
|---|---|
boolean |
getInitAsNaiveBayes()
Gets whether to init as naive bayes |
int |
getMaxNrOfParents()
Gets the max number of parents. |
java.lang.String[] |
getOptions()
Gets the current settings of the search algorithm. |
boolean |
getRandomOrder()
Get random order flag |
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 search algorithm. |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
java.lang.String |
randomOrderTipText()
|
void |
search(BayesNet bayesNet,
Instances instances)
search determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph. |
void |
setInitAsNaiveBayes(boolean bInitAsNaiveBayes)
Sets whether to init as naive bayes |
void |
setMaxNrOfParents(int nMaxNrOfParents)
Sets the max number of parents |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setRandomOrder(boolean bRandomOrder)
Set random order flag |
| 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 |
| Constructor Detail |
|---|
public K2()
| Method Detail |
|---|
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandler
public void search(BayesNet bayesNet,
Instances instances)
throws java.lang.Exception
bayesNet - the networkinstances - the data to work with
java.lang.Exception - if something goes wrongpublic void setMaxNrOfParents(int nMaxNrOfParents)
nMaxNrOfParents - the max number of parentspublic int getMaxNrOfParents()
public void setInitAsNaiveBayes(boolean bInitAsNaiveBayes)
bInitAsNaiveBayes - whether to init as naive bayespublic boolean getInitAsNaiveBayes()
public void setRandomOrder(boolean bRandomOrder)
bRandomOrder - the random order flagpublic boolean getRandomOrder()
public java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class GlobalScoreSearchAlgorithm
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-N Initial structure is empty (instead of Naive Bayes)
-P <nr of parents> Maximum number of parents
-R Random order. (default false)
-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)
setOptions in interface OptionHandlersetOptions in class GlobalScoreSearchAlgorithmoptions - 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 OptionHandlergetOptions in class GlobalScoreSearchAlgorithmpublic java.lang.String randomOrderTipText()
public java.lang.String globalInfo()
globalInfo in class GlobalScoreSearchAlgorithmpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class GlobalScoreSearchAlgorithm
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