SMOTEBoost

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.IteratedSingleClassifierEnhancer
    - - weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
      - weka.classifiers.meta.SMOTEBoost

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.classifiers.Sourcable, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

public class SMOTEBoost
extends weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
implements weka.core.WeightedInstancesHandler, weka.classifiers.Sourcable, weka.core.TechnicalInformationHandler

Class for boosting a binary class classifier using the SMOTEBoost method.
BibTeX:

 @article{N.V. Chawla and  A. Lazarevic and  L.O. Hall and  K.W. Bowyer},
 title = {SMOTEBoost: Improving prediction of the minority class in boosting},
 booktitle = {7th European Conference on Principles and Practice of Knowledge Discovery in Databases({PKDD 2003})},
 city = {Cavtat Dubrovnik},
 country = {Croatia},
 pages = {107--119},
 year = {2003}
 }

Valid options are:

 -smoteS <num>
  Specifies the random number seed
  (default 1)

 -smoteP <percentage>
  Specifies percentage of SMOTE instances to create.
  (default 100.0)

 -K <nearest-neighbors>
  Specifies the number of nearest neighbors to use.
  (default 5)

 -C <value-index>
  Specifies the index of the nominal class value to SMOTE
  (default 0: auto-detect non-empty minority class))

 -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)

 -Q
  Use resampling for boosting.

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 Options specific to classifier weka.classifiers.trees.DecisionStump:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Options after -- are passed to the designated classifier.

Version:: $Revision: 1 $
Author:: Sergio García Charameli, Daniel Rodríguez - University of Alcalá
See Also:: Serialized Form

Constructor Summary

Constructors
Constructor and Description

SMOTEBoost()
Constructor.

Constructors
Constructor and Description
`SMOTEBoost()` Constructor.

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances data)` Boosting method.
`double[]`	`distributionForInstance(weka.core.Instance instance)` Calculates the class membership probabilities for the given test instance.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`java.lang.String`	`getSMOTE_ClassValue()` Gets the index of the class value to which SMOTE should be applied.
`int`	`getSMOTE_NearestNeighbors()` Gets the number of nearest neighbors to use.
`double`	`getSMOTE_Percentage()` Gets the percentage of SMOTE instances to create.
`int`	`getSMOTE_RandomSeed()` Gets the random number seed.
`weka.core.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`	`getUseResampling()` Get whether resampling is turned on
`int`	`getWeightThreshold()` Get the degree of weight thresholding
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`weka.filters.supervised.instance.SMOTE`	`initSMOTE()`
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSMOTE_ClassValue(java.lang.String value)` Sets the index of the class value to which SMOTE should be applied.
`void`	`setSMOTE_NearestNeighbors(int value)` Sets the number of nearest neighbors to use.
`void`	`setSMOTE_Percentage(double value)` Sets the percentage of SMOTE instances to create.
`void`	`setSMOTE_RandomSeed(int value)` Sets the random number seed.
`void`	`setUseResampling(boolean r)` Set resampling mode
`void`	`setWeightThreshold(int threshold)` Set weight threshold
`java.lang.String`	`SMOTE_classValueTipText()` Returns the tip text for this property.
`java.lang.String`	`SMOTE_nearestNeighborsTipText()` Returns the tip text for this property.
`java.lang.String`	`SMOTE_percentageTipText()` Returns the tip text for this property.
`java.lang.String`	`SMOTE_randomSeedTipText()` Returns the tip text for this property.
`java.lang.String`	`toSource(java.lang.String className)` Returns the boosted model as Java source code.
`java.lang.String`	`toString()` Returns description of the boosted classifier.
`java.lang.String`	`useResamplingTipText()` Returns the tip text for this property
`java.lang.String`	`weightThresholdTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier

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

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

- Constructor Detail
  - SMOTEBoost
```
public SMOTEBoost()
```
    Constructor.
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public weka.core.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 weka.core.TechnicalInformationHandler
    
    Returns:
    the technical information about this class
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
    
    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:
```
 -smoteS <num>
  Specifies the random number seed
  (default 1)
 
```
```
 -smoteP <percentage>
  Specifies percentage of SMOTE instances to create.
  (default 100.0)
 
```
```
 -K <nearest-neighbors>
  Specifies the number of nearest neighbors to use.
  (default 5)
 
```
```
 -C <value-index>
  Specifies the index of the nominal class value to SMOTE
  (default 0: auto-detect non-empty minority class))
 
```
```
 -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 
```
```
 -Q
  Use resampling for boosting.
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -I <num>
  Number of iterations.
  (default 10)
 
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)
 
```
```
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
    Options after -- are passed to the designated classifier.
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
    
    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 weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
    
    Returns:
    an array of strings suitable for passing to setOptions
  - weightThresholdTipText
```
public java.lang.String weightThresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setWeightThreshold
```
public void setWeightThreshold(int threshold)
```
    Set weight threshold
    
    Parameters:
    threshold - the percentage of weight mass used for training
  - getWeightThreshold
```
public int getWeightThreshold()
```
    Get the degree of weight thresholding
    
    Returns:
    the percentage of weight mass used for training
  - useResamplingTipText
```
public java.lang.String useResamplingTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseResampling
```
public void setUseResampling(boolean r)
```
    Set resampling mode
    
    Parameters:
    r - true if resampling should be done
  - getUseResampling
```
public boolean getUseResampling()
```
    Get whether resampling is turned on
    
    Returns:
    true if resampling output is on
  - SMOTE_randomSeedTipText
```
public java.lang.String SMOTE_randomSeedTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSMOTE_RandomSeed
```
public int getSMOTE_RandomSeed()
```
    Gets the random number seed.
    
    Returns:
    the random number seed.
  - setSMOTE_RandomSeed
```
public void setSMOTE_RandomSeed(int value)
```
    Sets the random number seed.
    
    Parameters:
    value - the new random number seed.
  - SMOTE_percentageTipText
```
public java.lang.String SMOTE_percentageTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSMOTE_Percentage
```
public void setSMOTE_Percentage(double value)
```
    Sets the percentage of SMOTE instances to create.
    
    Parameters:
    value - the percentage to use
  - getSMOTE_Percentage
```
public double getSMOTE_Percentage()
```
    Gets the percentage of SMOTE instances to create.
    
    Returns:
    the percentage of SMOTE instances to create
  - SMOTE_nearestNeighborsTipText
```
public java.lang.String SMOTE_nearestNeighborsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSMOTE_NearestNeighbors
```
public void setSMOTE_NearestNeighbors(int value)
```
    Sets the number of nearest neighbors to use.
    
    Parameters:
    value - the number of nearest neighbors to use
  - getSMOTE_NearestNeighbors
```
public int getSMOTE_NearestNeighbors()
```
    Gets the number of nearest neighbors to use.
    
    Returns:
    the number of nearest neighbors to use
  - SMOTE_classValueTipText
```
public java.lang.String SMOTE_classValueTipText()
```
    Returns the tip text for this property.
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSMOTE_ClassValue
```
public void setSMOTE_ClassValue(java.lang.String value)
```
    Sets the index of the class value to which SMOTE should be applied.
    
    Parameters:
    value - the class value index
  - getSMOTE_ClassValue
```
public java.lang.String getSMOTE_ClassValue()
```
    Gets the index of the class value to which SMOTE should be applied.
    
    Returns:
    the index of the clas value to which SMOTE should be applied
  - initSMOTE
```
public weka.filters.supervised.instance.SMOTE initSMOTE()
```
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.SingleClassifierEnhancer
    
    Returns:
    the capabilities of this classifier
    See Also:
    Capabilities
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception
```
    Boosting method.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Overrides:
    
    buildClassifier in class weka.classifiers.IteratedSingleClassifierEnhancer
    
    Parameters:
    data - the training data to be used for generating the boosted classifier.
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    instance - the instance to be classified
    
    Returns:
    predicted class probability distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - toSource
```
public java.lang.String toSource(java.lang.String className)
                          throws java.lang.Exception
```
    Returns the boosted model as Java source code.
    
    Specified by:
    
    toSource in interface weka.classifiers.Sourcable
    
    Parameters:
    className - the classname of the generated class
    
    Returns:
    the tree as Java source code
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - toString
```
public java.lang.String toString()
```
    Returns description of the boosted classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    description of the boosted classifier as a string
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    argv - the options

Class SMOTEBoost

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Constructor Detail

SMOTEBoost

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

weightThresholdTipText

setWeightThreshold

getWeightThreshold

useResamplingTipText

setUseResampling

getUseResampling

SMOTE_randomSeedTipText

getSMOTE_RandomSeed

setSMOTE_RandomSeed

SMOTE_percentageTipText

setSMOTE_Percentage

getSMOTE_Percentage

SMOTE_nearestNeighborsTipText

setSMOTE_NearestNeighbors

getSMOTE_NearestNeighbors

SMOTE_classValueTipText

setSMOTE_ClassValue

getSMOTE_ClassValue

initSMOTE

getCapabilities

buildClassifier

distributionForInstance

toSource

toString

getRevision

main