LBR

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## 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: 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

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