QuickDDIterative

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.mi.QuickDDIterative

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.MultiInstanceCapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler
```
public class QuickDDIterative
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.MultiInstanceCapabilitiesHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler
```
Modified, faster, iterative version of the basic diverse density algorithm. Uses only instances from positive bags as candidate diverse density maxima. Picks best instance based on current scaling vector, then optimizes scaling vector. Once vector has been found, picks new best point based on new scaling vector (if the number of desired iterations is greater than one). Performs one iteration by default (Scaling Once). For good results, try boosting it with RealAdaBoost, setting the maximum probability of the negative class to 0.5 and enabling consideration of both classes as the positive class. Note that standardization of attributes is default, but normalization can work better.

James R. Foulds, Eibe Frank: Speeding up and boosting diverse density learning. In: Proc 13th International Conference on Discovery Science, 102-116, 2010.
BibTeX:
```
 @inproceedings{Foulds2010,
    author = {James R. Foulds and Eibe Frank},
    booktitle = {Proc 13th International Conference on Discovery Science},
    pages = {102-116},
    publisher = {Springer},
    title = {Speeding up and boosting diverse density learning},
    year = {2010}
 }
 
```
Valid options are:
```
 -D
  Turn on debugging output.
 
```
```
 -N <num>
  Whether to 0=normalize/1=standardize/2=neither.
  (default 1=standardize)
 
```
```
 -S <num>
  The initial scaling factor (constant for all attributes).
 
```
```
 -M <num>
  Maximum probability of negative class (default 1).
 
```
```
 -I <num>
  The maximum number of iterations to perform (default 1).
 
```
```
 -C
  Consider both classes as positive classes. (default: only last class).
 
```
Version:

$Revision: 10369 $

Author:

James Foulds, Xin Xu, Eibe Frank

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`FILTER_NONE` No normalization/standardization
`static int`	`FILTER_NORMALIZE` Normalize training data
`static int`	`FILTER_STANDARDIZE` Standardize training data
`static weka.core.Tag[]`	`TAGS_FILTER` The filter to apply to the training data

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

QuickDDIterative()

Constructors
Constructor and Description
`QuickDDIterative()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances train)` Builds the classifier
`java.lang.String`	`considerBothClassesTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(weka.core.Instance exmp)` Computes the distribution for a given exemplar
`java.lang.String`	`filterTypeTipText()` Returns the tip text for this property
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getConsiderBothClasses()` Get wether to consider both classes as "positive" class in turn.
`weka.core.SelectedTag`	`getFilterType()` Gets how the training data will be transformed.
`int`	`getMaxIterations()`
`double`	`getMaxProbNegativeClass()` Get the maximum probability for the negative class.
`weka.core.Capabilities`	`getMultiInstanceCapabilities()` Returns the capabilities of this multi-instance classifier for the relational data.
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`java.lang.String`	`getRevision()` Returns the revision string
`double`	`getScalingFactor()` Get the scaling factor for the Gaussian-like function at the target point.
`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.
`java.lang.String`	`globalInfo()` Returns a string describing this filter
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`maxIterationsTipText()` Returns the tip text for this property
`java.lang.String`	`maxProbNegativeClassTipText()` Returns the tip text for this property
`java.lang.String`	`scalingFactorTipText()` Returns the tip text for this property
`void`	`setConsiderBothClasses(boolean b)` Set wether to consider both classes as "positive" class in turn.
`void`	`setFilterType(weka.core.SelectedTag newType)` Sets how the training data will be transformed.
`void`	`setMaxIterations(int maxIterations)`
`void`	`setMaxProbNegativeClass(double r)` Set the maximum probability for the negative class.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setScalingFactor(double scale)` Set the scaling factor for the Gaussian-like function at the target point.
`java.lang.String`	`toString()` Gets a string describing the classifier.

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Field Detail
  - FILTER_NORMALIZE
```
public static final int FILTER_NORMALIZE
```
    Normalize training data
    
    See Also:
    
    Constant Field Values
  - FILTER_STANDARDIZE
```
public static final int FILTER_STANDARDIZE
```
    Standardize training data
    
    See Also:
    
    Constant Field Values
  - FILTER_NONE
```
public static final int FILTER_NONE
```
    No normalization/standardization
    
    See Also:
    
    Constant Field Values
  - TAGS_FILTER
```
public static final weka.core.Tag[] TAGS_FILTER
```
    The filter to apply to the training data
- Constructor Detail
  - QuickDDIterative
```
public QuickDDIterative()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this filter
    
    Returns:
    
    a description of the filter 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<weka.core.Option> listOptions()
```
    Returns an enumeration describing the available options
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    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:
```
 -N <num>
  Whether to 0=normalize/1=standardize/2=neither.
  (default 1=standardize)
 
```
```
 -S <num>
  The initial scaling factor (constant for all attributes).
 
```
```
 -M <num>
  Maximum probability of negative class (default 1).
 
```
```
 -I <num>
  The maximum number of iterations to perform (default 1).
 
```
```
 -C
  Consider both classes as positive classes. (default: only last class).
 
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    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.AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - filterTypeTipText
```
public java.lang.String filterTypeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getFilterType
```
public weka.core.SelectedTag getFilterType()
```
    Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
    
    Returns:
    
    the filtering mode
  - setFilterType
```
public void setFilterType(weka.core.SelectedTag newType)
```
    Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
    
    Parameters:
    
    newType - the new filtering mode
  - scalingFactorTipText
```
public java.lang.String scalingFactorTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setScalingFactor
```
public void setScalingFactor(double scale)
```
    Set the scaling factor for the Gaussian-like function at the target point. Squared distances between the target point and other points will be divided by the square of this value.
    
    Parameters:
    
    scale -
  - getScalingFactor
```
public double getScalingFactor()
```
    Get the scaling factor for the Gaussian-like function at the target point.
    
    Returns:
    
    the scaling factor.
  - maxProbNegativeClassTipText
```
public java.lang.String maxProbNegativeClassTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMaxProbNegativeClass
```
public void setMaxProbNegativeClass(double r)
```
    Set the maximum probability for the negative class.
  - getMaxProbNegativeClass
```
public double getMaxProbNegativeClass()
```
    Get the maximum probability for the negative class.
  - considerBothClassesTipText
```
public java.lang.String considerBothClassesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setConsiderBothClasses
```
public void setConsiderBothClasses(boolean b)
```
    Set wether to consider both classes as "positive" class in turn.
  - getConsiderBothClasses
```
public boolean getConsiderBothClasses()
```
    Get wether to consider both classes as "positive" class in turn.
  - maxIterationsTipText
```
public java.lang.String maxIterationsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Parameters:
    
    m_maxIterations - the m_maxIterations to set
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns:
    
    the m_maxIterations
  - 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.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - getMultiInstanceCapabilities
```
public weka.core.Capabilities getMultiInstanceCapabilities()
```
    Returns the capabilities of this multi-instance classifier for the relational data.
    
    Specified by:
    
    getMultiInstanceCapabilities in interface weka.core.MultiInstanceCapabilitiesHandler
    
    Returns:
    
    the capabilities of this object
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(weka.core.Instances train)
                     throws java.lang.Exception
```
    Builds the classifier
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    train - 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 exmp)
                                 throws java.lang.Exception
```
    Computes the distribution for a given exemplar
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    exmp - the exemplar for which distribution is computed
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if the distribution can't be computed successfully
  - toString
```
public java.lang.String toString()
```
    Gets a string describing the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string describing the classifer built.
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain the command line arguments to the scheme (see Evaluation)
  - 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

Class QuickDDIterative

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

TAGS_FILTER

Constructor Detail

QuickDDIterative

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

filterTypeTipText

getFilterType

setFilterType

scalingFactorTipText

setScalingFactor

getScalingFactor

maxProbNegativeClassTipText

setMaxProbNegativeClass

getMaxProbNegativeClass

considerBothClassesTipText

setConsiderBothClasses

getConsiderBothClasses

maxIterationsTipText

setMaxIterations

getMaxIterations

getCapabilities

getMultiInstanceCapabilities

buildClassifier

distributionForInstance

toString

main

getRevision