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 Description

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

Constructor Summary

Constructors
Constructor Description

LBR()

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	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

Class LBR

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

LBR

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

distributionForInstance

toString

leaveOneOut

localNaiveBayes

localDistributionForInstance

binomP

getRevision

main