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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.lazy.LBR
public class LBR
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.
@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
Nested Class Summary | |
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class |
LBR.Indexes
Class for handling instances and the associated attributes. |
Constructor Summary | |
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LBR()
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Method Summary | |
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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()
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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 |
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classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public LBR()
Method Detail |
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public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class Classifier
Capabilities
public void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier
in class Classifier
instances
- set of instances serving as training data
java.lang.Exception
- if the preparation has not been generated.public double[] distributionForInstance(Instance testInstance) throws java.lang.Exception
distributionForInstance
in class Classifier
testInstance
- the instance to be classified
java.lang.Exception
- if distribution can't be computedpublic java.lang.String toString()
toString
in class java.lang.Object
public int leaveOneOut(LBR.Indexes instanceIndex, int[][][] counts, int[] priors, boolean[] errorFlags) throws java.lang.Exception
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
java.lang.Exception
- if something goes wrongpublic void localNaiveBayes(LBR.Indexes instanceIndex) throws java.lang.Exception
Richard Duda and Peter Hart (1973).Pattern Classification and Scene Analysis. Wiley, New York. This method only get m_Counts and m_Priors.
instanceIndex
- set of instances serving as training data
java.lang.Exception
- if m_Counts and m_Priors have not been
generated successfullypublic double[] localDistributionForInstance(Instance instance, LBR.Indexes instanceIndex) throws java.lang.Exception
instance
- the instance to be classifiedinstanceIndex
-
java.lang.Exception
- if distribution can't be computedpublic double binomP(double r, double n, double p) throws java.lang.Exception
r
- n
- p
-
java.lang.Exception
- if computation failspublic java.lang.String getRevision()
getRevision
in interface RevisionHandler
public static void main(java.lang.String[] argv)
argv
- the options
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