weka.classifiers.lazy
Class LBR

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.lazy.LBR
All Implemented Interfaces:
java.io.Serializable, java.lang.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: 1.12 $
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:
Serialized Form

Nested Class Summary
 class LBR.Indexes
          Class for handling instances and the associated attributes.
 
Constructor Summary
LBR()
           
 
Method Summary
 double binomP(double r, double n, double p)
          Significance test binomp:
 void buildClassifier(Instances instances)
          For lazy learning, building classifier is only to prepare their inputs until classification time.
 double[] distributionForInstance(Instance testInstance)
          Calculates the class membership probabilities for the given test instance.
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 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()
           
 int leaveOneOut(LBR.Indexes instanceIndex, int[][][] counts, int[] priors, boolean[] errorFlags)
          Leave-one-out strategy.
 double[] localDistributionForInstance(Instance instance, LBR.Indexes instanceIndex)
          Calculates the class membership probabilities.
 void localNaiveBayes(LBR.Indexes instanceIndex)
          Class for building and using a simple Naive Bayes classifier.
static void main(java.lang.String[] argv)
          Main method for testing this class.
 java.lang.String toString()
          Returns a description of the classifier.
 
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Constructor Detail

LBR

public LBR()
Method Detail

globalInfo

public java.lang.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:
Capabilities

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.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:
java.lang.Exception - if the preparation has not been generated.

distributionForInstance

public double[] distributionForInstance(Instance testInstance)
                                 throws java.lang.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:
java.lang.Exception - if distribution can't be computed

toString

public java.lang.String toString()
Returns a description of the classifier.

Overrides:
toString in class java.lang.Object
Returns:
a description of the classifier as a string.

leaveOneOut

public int leaveOneOut(LBR.Indexes instanceIndex,
                       int[][][] counts,
                       int[] priors,
                       boolean[] errorFlags)
                throws java.lang.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:
java.lang.Exception - if something goes wrong

localNaiveBayes

public void localNaiveBayes(LBR.Indexes instanceIndex)
                     throws java.lang.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:
java.lang.Exception - if m_Counts and m_Priors have not been generated successfully

localDistributionForInstance

public double[] localDistributionForInstance(Instance instance,
                                             LBR.Indexes instanceIndex)
                                      throws java.lang.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:
java.lang.Exception - if distribution can't be computed

binomP

public double binomP(double r,
                     double n,
                     double p)
              throws java.lang.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:
java.lang.Exception - if computation fails

getRevision

public java.lang.String getRevision()
Returns the revision string.

Specified by:
getRevision in interface RevisionHandler
Returns:
the revision

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the options