AODEsr

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.bayes.AODEsr

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, UpdateableClassifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class AODEsr
extends Classifier
implements OptionHandler, WeightedInstancesHandler, UpdateableClassifier, TechnicalInformationHandler

AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see:
Fei Zheng, Geoffrey I. Webb: Efficient Lazy Elimination for Averaged-One Dependence Estimators. In: Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006), 1113-1120, 2006.

BibTeX:

 @inproceedings{Zheng2006,
    author = {Fei Zheng and Geoffrey I. Webb},
    booktitle = {Proceedings of the Twenty-third International Conference on Machine  Learning (ICML 2006)},
    pages = {1113-1120},
    publisher = {ACM Press},
    title = {Efficient Lazy Elimination for Averaged-One Dependence Estimators},
    year = {2006},
    ISBN = {1-59593-383-2}
 }

Valid options are:

 -D
  Output debugging information

 -C
  Impose a critcal value for specialization-generalization relationship
  (default is 50)

 -F
  Impose a frequency limit for superParents
  (default is 1)

 -L
  Using Laplace estimation
  (default is m-esimation (m=1))

 -M
  Weight value for m-estimation
  (default is 1.0)

Version:: $Revision: 5516 $
Author:: Fei Zheng, Janice Boughton
See Also:: Serialized Form

Constructor Summary

Constructors
Constructor and Description

AODEsr()

Constructors
Constructor and Description
`AODEsr()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances instances)` Generates the classifier.
`java.lang.String`	`criticalValueTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`java.lang.String`	`frequencyLimitTipText()` Returns the tip text for this property
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`int`	`getCriticalValue()` Gets the critical value.
`int`	`getFrequencyLimit()` Gets the frequency limit.
`double`	`getMestWeight()` Gets the weight used in m-estimate
`java.lang.String[]`	`getOptions()` Gets the current settings 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.
`boolean`	`getUseLaplace()` Gets if laplace correction is being used.
`java.lang.String`	`globalInfo()` Returns a string describing this classifier
`double`	`LaplaceEstimate(double frequency, double total, double numValues)` Returns the probability estimate, using laplace correction
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`double`	`MEstimate(double frequency, double total, double numValues)` Returns the probability estimate, using m-estimate
`java.lang.String`	`mestWeightTipText()` Returns the tip text for this property
`double`	`NBconditionalProb(Instance instance, int classVal)` Calculates the probability of the specified class for the given test instance, using naive Bayes.
`void`	`setCriticalValue(int c)` Sets the critical value
`void`	`setFrequencyLimit(int f)` Sets the frequency limit
`void`	`setMestWeight(double w)` Sets the weight for m-estimate
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setUseLaplace(boolean value)` Sets if laplace correction is to be used.
`java.lang.String`	`toString()` Returns a description of the classifier.
`void`	`updateClassifier(Instance instance)` Updates the classifier with the given instance.
`java.lang.String`	`useLaplaceTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - AODEsr
```
public AODEsr()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this classifier
    
    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
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    instances - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the classifier has not been generated successfully
  - updateClassifier
```
public void updateClassifier(Instance instance)
```
    Updates the classifier with the given instance.
    
    Specified by:
    
    updateClassifier in interface UpdateableClassifier
    
    Parameters:
    
    instance - the new training instance to include in the model
    
    Throws:
    
    java.lang.Exception - if the instance could not be incorporated in the model.
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    java.lang.Exception - if there is a problem generating the prediction
  - NBconditionalProb
```
public double NBconditionalProb(Instance instance,
                                int classVal)
                         throws java.lang.Exception
```
    Calculates the probability of the specified class for the given test instance, using naive Bayes.
    
    Parameters:
    
    instance - the instance to be classified
    
    classVal - the class for which to calculate the probability
    
    Returns:
    
    predicted class probability
    
    Throws:
    
    java.lang.Exception - if there is a problem generating the prediction
  - MEstimate
```
public double MEstimate(double frequency,
                        double total,
                        double numValues)
```
    Returns the probability estimate, using m-estimate
    
    Parameters:
    
    frequency - frequency of value of interest
    
    total - count of all values
    
    numValues - number of different values
    
    Returns:
    
    the probability estimate
  - LaplaceEstimate
```
public double LaplaceEstimate(double frequency,
                              double total,
                              double numValues)
```
    Returns the probability estimate, using laplace correction
    
    Parameters:
    
    frequency - frequency of value of interest
    
    total - count of all values
    
    numValues - number of different values
    
    Returns:
    
    the probability estimate
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class Classifier
    
    Returns:
    
    an enumeration of all the available options
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -D
  Output debugging information
 
```
```
 -C
  Impose a critcal value for specialization-generalization relationship
  (default is 50)
```
```
 -F
  Impose a frequency limit for superParents
  (default is 1)
```
```
 -L
  Using Laplace estimation
  (default is m-esimation (m=1))
```
```
 -M
  Weight value for m-estimation
  (default is 1.0)
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - mestWeightTipText
```
public java.lang.String mestWeightTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMestWeight
```
public void setMestWeight(double w)
```
    Sets the weight for m-estimate
    
    Parameters:
    
    w - the weight
  - getMestWeight
```
public double getMestWeight()
```
    Gets the weight used in m-estimate
    
    Returns:
    
    the weight for m-estimation
  - useLaplaceTipText
```
public java.lang.String useLaplaceTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getUseLaplace
```
public boolean getUseLaplace()
```
    Gets if laplace correction is being used.
    
    Returns:
    
    Value of m_Laplace.
  - setUseLaplace
```
public void setUseLaplace(boolean value)
```
    Sets if laplace correction is to be used.
    
    Parameters:
    
    value - Value to assign to m_Laplace.
  - frequencyLimitTipText
```
public java.lang.String frequencyLimitTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setFrequencyLimit
```
public void setFrequencyLimit(int f)
```
    Sets the frequency limit
    
    Parameters:
    
    f - the frequency limit
  - getFrequencyLimit
```
public int getFrequencyLimit()
```
    Gets the frequency limit.
    
    Returns:
    
    the frequency limit
  - criticalValueTipText
```
public java.lang.String criticalValueTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCriticalValue
```
public void setCriticalValue(int c)
```
    Sets the critical value
    
    Parameters:
    
    c - the critical value
  - getCriticalValue
```
public int getCriticalValue()
```
    Gets the critical value.
    
    Returns:
    
    the critical value
  - 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.
  - 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 AODEsr

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

AODEsr

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

updateClassifier

distributionForInstance

NBconditionalProb

MEstimate

LaplaceEstimate

listOptions

setOptions

getOptions

mestWeightTipText

setMestWeight

getMestWeight

useLaplaceTipText

getUseLaplace

setUseLaplace

frequencyLimitTipText

setFrequencyLimit

getFrequencyLimit

criticalValueTipText

setCriticalValue

getCriticalValue

toString

getRevision

main