weka.classifiers.lazy

Class LBR

java.lang.Object

weka.classifiers.Classifier

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: 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:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	LBR.Indexes Class for handling instances and the associated attributes.

Constructor Summary

Constructors

Constructor and Description
LBR()

Method Summary

Modifier and Type	Method and Description
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

Overrides:

getRevision in class Classifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options