weka.classifiers.lazy
Class LBR

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.lazy.LBR

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable

public class LBR

extends Classifier

Lazy Bayesian Rules implement a lazy learning approach to lessening the attribute-independence assumption of naive Bayes. For each object to be classified, LBR selects a set of attributes for which the attribute independence assumption should not be made. All remaining attributes are treated as independent of each other given the class and the selected set of attributes. LBR has demonstrated very high accuracy. Its training time is low but its classification time is high due to the use of a lazy methodology. This implementation does not include caching, that can substantially reduce classification time when multiple classifications are performed for a single training set. For more information, see

Zijian Zheng & G. Webb, (2000). Lazy Learning of Bayesian Rules. Machine Learning, 41(1): 53-84.

Version:

$Revision: 1.4 $

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.
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, 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

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:

counts - serving as all the counts of training data.

priors - serving as the number of instances in each class.

Returns:

error flag array about each instance.

java.lang.Exception

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.

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

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:

Returns:

returns the probability of obtaining r or fewer out of n if the probability of an event is p.

java.lang.Exception

main

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

Main method for testing this class.

Parameters:

argv - the options