Class LBR

All Implemented Interfaces:
Serializable, Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class LBR extends Classifier implements TechnicalInformationHandler
Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified.

For more information, see:

Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.

BibTeX:

 @article{Zheng2000,
    author = {Zijian Zheng and G. Webb},
    journal = {Machine Learning},
    number = {1},
    pages = {53-84},
    title = {Lazy Learning of Bayesian Rules},
    volume = {4},
    year = {2000}
 }
 

Valid options are:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
Version:
$Revision: 5525 $
Author:
Zhihai Wang (zhw@deakin.edu.au) : July 2001 implemented the algorithm, Jason Wells (wells@deakin.edu.au) : November 2001 added instance referencing via indexes
See Also:
  • Constructor Details

    • LBR

      public LBR()
  • Method Details

    • globalInfo

      public String globalInfo()
      Returns:
      a description of the classifier suitable for displaying in the explorer/experimenter gui
    • 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
    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Overrides:
      getCapabilities in class Classifier
      Returns:
      the capabilities of this classifier
      See Also:
    • buildClassifier

      public void buildClassifier(Instances instances) throws Exception
      For lazy learning, building classifier is only to prepare their inputs until classification time.
      Specified by:
      buildClassifier in class Classifier
      Parameters:
      instances - set of instances serving as training data
      Throws:
      Exception - if the preparation has not been generated.
    • distributionForInstance

      public double[] distributionForInstance(Instance testInstance) throws Exception
      Calculates the class membership probabilities for the given test instance. This is the most important method for Lazy Bayesian Rule algorithm.
      Overrides:
      distributionForInstance in class Classifier
      Parameters:
      testInstance - the instance to be classified
      Returns:
      predicted class probability distribution
      Throws:
      Exception - if distribution can't be computed
    • toString

      public String toString()
      Returns a description of the classifier.
      Overrides:
      toString in class Object
      Returns:
      a description of the classifier as a string.
    • leaveOneOut

      public int leaveOneOut(LBR.Indexes instanceIndex, int[][][] counts, int[] priors, boolean[] errorFlags) throws Exception
      Leave-one-out strategy. For a given sample data set with n instances, using (n - 1) instances by leaving one out and tested on the single remaining case. This is repeated n times in turn. The final "Error" is the sum of the instances to be classified incorrectly.
      Parameters:
      instanceIndex - set of instances serving as training data.
      counts - serving as all the counts of training data.
      priors - serving as the number of instances in each class.
      errorFlags - for the errors
      Returns:
      error flag array about each instance.
      Throws:
      Exception - if something goes wrong
    • localNaiveBayes

      public void localNaiveBayes(LBR.Indexes instanceIndex) throws Exception
      Class for building and using a simple Naive Bayes classifier. For more information, see

      Richard Duda and Peter Hart (1973).Pattern Classification and Scene Analysis. Wiley, New York. This method only get m_Counts and m_Priors.

      Parameters:
      instanceIndex - set of instances serving as training data
      Throws:
      Exception - if m_Counts and m_Priors have not been generated successfully
    • localDistributionForInstance

      public double[] localDistributionForInstance(Instance instance, LBR.Indexes instanceIndex) throws Exception
      Calculates the class membership probabilities. for the given test instance.
      Parameters:
      instance - the instance to be classified
      instanceIndex -
      Returns:
      predicted class probability distribution
      Throws:
      Exception - if distribution can't be computed
    • binomP

      public double binomP(double r, double n, double p) throws Exception
      Significance test binomp:
      Parameters:
      r -
      n -
      p -
      Returns:
      returns the probability of obtaining r or fewer out of n if the probability of an event is p.
      Throws:
      Exception - if computation fails
    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class Classifier
      Returns:
      the revision
    • main

      public static void main(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - the options