Package weka.classifiers.bayes.net.search.local

Class K2

  • All Implemented Interfaces:
    java.io.Serializable, OptionHandler, RevisionHandler, TechnicalInformationHandler
    public class K2
extends LocalScoreSearchAlgorithm
implements TechnicalInformationHandler
    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.

    BibTeX: 

     @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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
    Version:
    $Revision: 1.8 $
    Author:
    Remco Bouckaert (rrb@xm.co.nz)
    See Also:
    Serialized Form

    • Constructor Detail

      • K2

        public K2()

    • 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

      • search

        public void search​(BayesNet bayesNet,
                   Instances instances)
            throws java.lang.Exception
        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.
        Parameters:
        bayesNet - the network
        instances - the data to work with
        Throws:
        java.lang.Exception - if something goes wrong

      • setMaxNrOfParents

        public void setMaxNrOfParents​(int nMaxNrOfParents)
        Sets the max number of parents
        Parameters:
        nMaxNrOfParents - the max number of parents

      • getMaxNrOfParents

        public int getMaxNrOfParents()
        Gets the max number of parents.
        Returns:
        the max number of parents

      • setInitAsNaiveBayes

        public void setInitAsNaiveBayes​(boolean bInitAsNaiveBayes)
        Sets whether to init as naive bayes
        Parameters:
        bInitAsNaiveBayes - whether to init as naive bayes

      • getInitAsNaiveBayes

        public boolean getInitAsNaiveBayes()
        Gets whether to init as naive bayes
        Returns:
        whether to init as naive bayes

      • setRandomOrder

        public void setRandomOrder​(boolean bRandomOrder)
        Set random order flag
        Parameters:
        bRandomOrder - the random order flag

      • getRandomOrder

        public boolean getRandomOrder()
        Get random order flag
        Returns:
        the random order flag

      • setOptions

        public void setOptions​(java.lang.String[] options)
                throws java.lang.Exception
        Parses a given list of options.

        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 [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
        Specified by:
        setOptions in interface OptionHandler
        Overrides:
        setOptions in class LocalScoreSearchAlgorithm
        Parameters:
        options - the list of options as an array of strings
        Throws:
        java.lang.Exception - if an option is not supported

      • globalInfo

        public java.lang.String globalInfo()
        This will return a string describing the search algorithm.
        Overrides:
        globalInfo in class LocalScoreSearchAlgorithm
        Returns:
        The string.

      • randomOrderTipText

        public java.lang.String randomOrderTipText()
        Returns:
        a string to describe the RandomOrder option.