ComplementNaiveBayes

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

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
```
public class ComplementNaiveBayes
extends Classifier
implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler
```
Class for building and using a Complement class Naive Bayes classifier.

For more information see,

Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, 616-623, 2003.

P.S.: TF, IDF and length normalization transforms, as described in the paper, can be performed through weka.filters.unsupervised.StringToWordVector.
BibTeX:
```
 @inproceedings{Rennie2003,
    author = {Jason D. Rennie and Lawrence Shih and Jaime Teevan and David R. Karger},
    booktitle = {ICML},
    pages = {616-623},
    publisher = {AAAI Press},
    title = {Tackling the Poor Assumptions of Naive Bayes Text Classifiers},
    year = {2003}
 }
 
```
Valid options are:
```
 -N
  Normalize the word weights for each class
 
```
```
 -S
  Smoothing value to avoid zero WordGivenClass probabilities (default=1.0).
 
```
Version:

$Revision: 5516 $

Author:

Ashraf M. Kibriya (amk14@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

ComplementNaiveBayes()

Constructors
Constructor and Description
`ComplementNaiveBayes()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances instances)` Generates the classifier.
`double`	`classifyInstance(Instance instance)` Classifies a given instance.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getNormalizeWordWeights()` Returns true if the word weights for each class are to be normalized
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSmoothingParameter()` Gets the smoothing value to be used to avoid zero WordGivenClass probabilities.
`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()` Returns a string describing this classifier
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`normalizeWordWeightsTipText()` Returns the tip text for this property
`void`	`setNormalizeWordWeights(boolean doNormalize)` Sets whether if the word weights for each class should be normalized
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSmoothingParameter(double val)` Sets the smoothing value used to avoid zero WordGivenClass probabilities
`java.lang.String`	`smoothingParameterTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Prints out the internal model built by the classifier.

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

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

- Constructor Detail
  - ComplementNaiveBayes
```
public ComplementNaiveBayes()
```
- Method Detail
  - 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.
  - 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
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -N
  Normalize the word weights for each class
 
```
```
 -S
  Smoothing value to avoid zero WordGivenClass probabilities (default=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
  - getNormalizeWordWeights
```
public boolean getNormalizeWordWeights()
```
    Returns true if the word weights for each class are to be normalized
    
    Returns:
    
    true if the word weights are normalized
  - setNormalizeWordWeights
```
public void setNormalizeWordWeights(boolean doNormalize)
```
    Sets whether if the word weights for each class should be normalized
    
    Parameters:
    
    doNormalize - whether the word weights are to be normalized
  - normalizeWordWeightsTipText
```
public java.lang.String normalizeWordWeightsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSmoothingParameter
```
public double getSmoothingParameter()
```
    Gets the smoothing value to be used to avoid zero WordGivenClass probabilities.
    
    Returns:
    
    the smoothing value
  - setSmoothingParameter
```
public void setSmoothingParameter(double val)
```
    Sets the smoothing value used to avoid zero WordGivenClass probabilities
    
    Parameters:
    
    val - the new smooting value
  - smoothingParameterTipText
```
public java.lang.String smoothingParameterTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - 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 built successfully
  - classifyInstance
```
public double classifyInstance(Instance instance)
                        throws java.lang.Exception
```
    Classifies a given instance.
    The classification rule is:
    MinC(forAllWords(ti*Wci))
    where
    ti is the frequency of word i in the given instance
    Wci is the weight of word i in Class c.
    For more information see section 4.4 of the paper mentioned above in the classifiers description.
    
    Overrides:
    
    classifyInstance in class Classifier
    
    Parameters:
    
    instance - the instance to classify
    
    Returns:
    
    the index of the class the instance is most likely to belong.
    
    Throws:
    
    java.lang.Exception - if the classifier has not been built yet.
  - toString
```
public java.lang.String toString()
```
    Prints out the internal model built by the classifier. In this case it prints out the word weights calculated when building the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
  - 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 ComplementNaiveBayes

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

ComplementNaiveBayes

Method Detail

listOptions

getOptions

setOptions

getNormalizeWordWeights

setNormalizeWordWeights

normalizeWordWeightsTipText

getSmoothingParameter

setSmoothingParameter

smoothingParameterTipText

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

classifyInstance

toString

getRevision

main