EnsembleSelection

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.RandomizableClassifier
  - - weka.classifiers.meta.EnsembleSelection

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.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class EnsembleSelection
extends weka.classifiers.RandomizableClassifier
implements weka.core.TechnicalInformationHandler

Combines several classifiers using the ensemble selection method. For more information, see: Caruana, Rich, Niculescu, Alex, Crew, Geoff, and Ksikes, Alex, Ensemble Selection from Libraries of Models, The International Conference on Machine Learning (ICML'04), 2004. Implemented in Weka by Bob Jung and David Michael.

BibTeX:

 @inproceedings{RichCaruana2004,
    author = {Rich Caruana, Alex Niculescu, Geoff Crew, and Alex Ksikes},
    booktitle = {21st International Conference on Machine Learning},
    title = {Ensemble Selection from Libraries of Models},
    year = {2004}
 }

Our implementation of ensemble selection is a bit different from the other classifiers because we assume that the list of models to be trained is too large to fit in memory and that our base classifiers will need to be serialized to the file system (in the directory listed in the "workingDirectory option). We have adopted the term "model library" for this large set of classifiers keeping in line with the original paper.

If you are planning to use this classifier, we highly recommend you take a quick look at our FAQ/tutorial on the WIKI. There are a few things that are unique to this classifier that could trip you up. Otherwise, this method is a great way to get really great classifier performance without having to do too much parameter tuning. What is nice is that in the worst case you get a nice summary of how s large number of diverse models performed on your data set.

This class relies on the package weka.classifiers.meta.ensembleSelection.

When run from the Explorer or another GUI, the classifier depends on the package weka.gui.libraryEditor.

Valid options are:

 -L </path/to/modelLibrary>
  Specifies the Model Library File, continuing the list of all models.

 -W </path/to/working/directory>
  Specifies the Working Directory, where all models will be stored.

 -B <numModelBags>
  Set the number of bags, i.e., number of iterations to run 
  the ensemble selection algorithm.

 -E <modelRatio>
  Set the ratio of library models that will be randomly chosen 
  to populate each bag of models.

 -V <validationRatio>
  Set the ratio of the training data set that will be reserved 
  for validation.

 -H <hillClimbIterations>
  Set the number of hillclimbing iterations to be performed 
  on each model bag.

 -I <sortInitialization>
  Set the the ratio of the ensemble library that the sort 
  initialization algorithm will be able to choose from while 
  initializing the ensemble for each model bag

 -X <numFolds>
  Sets the number of cross-validation folds.

 -P <hillclimbMettric>
  Specify the metric that will be used for model selection 
  during the hillclimbing algorithm.
  Valid metrics are: 
   accuracy, rmse, roc, precision, recall, fscore, all

 -A <algorithm>
  Specifies the algorithm to be used for ensemble selection. 
  Valid algorithms are:
   "forward" (default) for forward selection.
   "backward" for backward elimination.
   "both" for both forward and backward elimination.
   "best" to simply print out top performer from the 
      ensemble library
   "library" to only train the models in the ensemble 
      library

 -R
  Flag whether or not models can be selected more than once 
  for an ensemble.

 -G
  Whether sort initialization greedily stops adding models 
  when performance degrades.

 -O
  Flag for verbose output. Prints out performance of all 
  selected models.

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

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

Version:: $Revision: 8108 $
Author:: Robert Jung, David Michael
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`ALGORITHM_BACKWARD`
`static int`	`ALGORITHM_BEST`
`static int`	`ALGORITHM_BUILD_LIBRARY`
`static int`	`ALGORITHM_FORWARD` The "enumeration" of the algorithms we can use.
`static int`	`ALGORITHM_FORWARD_BACKWARD`
`static weka.core.Tag[]`	`TAGS_ALGORITHM` defines metrics that can be chosen for hillclimbing
`static weka.core.Tag[]`	`TAGS_METRIC` defines metrics that can be chosen for hillclimbing

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

Constructor Summary

Constructors
Constructor and Description

EnsembleSelection()

Constructors
Constructor and Description
`EnsembleSelection()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`algorithmTipText()` Returns the tip text for this property
`void`	`buildClassifier(weka.core.Instances trainData)` Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
`double[]`	`distributionForInstance(weka.core.Instance instance)` Calculates the class membership probabilities for the given test instance.
`weka.core.SelectedTag`	`getAlgorithm()` Gets the algorithm
`weka.core.Capabilities`	`getCapabilities()` We return true for basically everything except for Missing class values, because we can't really answer for all the models in our library.
`static java.lang.String`	`getDefaultWorkingDirectory()` This method tries to find a reasonable path name for the ensemble working directory where models and files will be stored.
`boolean`	`getGreedySortInitialization()` Get the value of greedySortInitialization.
`int`	`getHillclimbIterations()` Gets the number of hillclimbIterations.
`weka.core.SelectedTag`	`getHillclimbMetric()` Gets the hill climbing metric.
`EnsembleSelectionLibrary`	`getLibrary()` Gets the ensemble library.
`double`	`getModelRatio()` Get the value of modelRatio.
`int`	`getNumFolds()` Gets the number of folds for the cross-validation.
`int`	`getNumModelBags()` Gets numModelBags.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`boolean`	`getReplacement()` Get the value of replacement.
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSortInitializationRatio()` Get the value of sortInitializationRatio.
`weka.core.TechnicalInformation`	`getTechnicalInformation()` Return the technical information.
`double`	`getValidationRatio()` Get the value of validationRatio.
`boolean`	`getVerboseOutput()` Get the value of verboseOutput.
`java.io.File`	`getWorkingDirectory()` Get the value of working directory.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.lang.String`	`greedySortInitializationTipText()` Returns the tip text for this property
`java.lang.String`	`hillclimbIterationsTipText()` Returns the tip text for this property
`java.lang.String`	`hillclimbMetricTipText()` Returns the tip text for this property
`java.lang.String`	`libraryTipText()` Returns the tip text for this property
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Executes the classifier from commandline.
`java.lang.String`	`modelRatioTipText()` Returns the tip text for this property
`java.lang.String`	`numFoldsTipText()` Returns the tip text for this property
`java.lang.String`	`numModelBagsTipText()` Returns the tip text for this property
`java.lang.String`	`replacementTipText()` Returns the tip text for this property
`void`	`setAlgorithm(weka.core.SelectedTag newType)` Sets the Algorithm to use
`void`	`setGreedySortInitialization(boolean newGreedySortInitialization)` Set the value of greedySortInitialization.
`void`	`setHillclimbIterations(int n)` Sets the number of hillclimbIterations.
`void`	`setHillclimbMetric(weka.core.SelectedTag newType)` Sets the hill climbing metric.
`void`	`setLibrary(EnsembleSelectionLibrary newLibrary)` Sets the ensemble library.
`void`	`setModelRatio(double v)` Set the value of modelRatio.
`void`	`setNumFolds(int numFolds)` Sets the number of folds for the cross-validation.
`void`	`setNumModelBags(int n)` Sets numModelBags.
`void`	`setOptions(java.lang.String[] options)` Valid options are:
`void`	`setReplacement(boolean newReplacement)` Set the value of replacement.
`void`	`setSortInitializationRatio(double v)` Set the value of sortInitializationRatio.
`void`	`setValidationRatio(double v)` Set the value of validationRatio.
`void`	`setVerboseOutput(boolean newVerboseOutput)` Set the value of verboseOutput.
`void`	`setWorkingDirectory(java.io.File newWorkingDirectory)` Set the value of working directory.
`java.lang.String`	`sortInitializationRatioTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Output a representation of this classifier
`java.lang.String`	`validationRatioTipText()` Returns the tip text for this property
`java.lang.String`	`verboseOutputTipText()` Returns the tip text for this property
`java.lang.String`	`workingDirectoryTipText()` Returns the tip text for this property

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

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
  - TAGS_METRIC
```
public static final weka.core.Tag[] TAGS_METRIC
```
    defines metrics that can be chosen for hillclimbing
  - ALGORITHM_FORWARD
```
public static final int ALGORITHM_FORWARD
```
    The "enumeration" of the algorithms we can use. Forward - forward selection. For hillclimb iterations,
    
    See Also:
    
    Constant Field Values
  - ALGORITHM_BACKWARD
```
public static final int ALGORITHM_BACKWARD
```
    See Also:
    
    Constant Field Values
  - ALGORITHM_FORWARD_BACKWARD
```
public static final int ALGORITHM_FORWARD_BACKWARD
```
    See Also:
    
    Constant Field Values
  - ALGORITHM_BEST
```
public static final int ALGORITHM_BEST
```
    See Also:
    
    Constant Field Values
  - ALGORITHM_BUILD_LIBRARY
```
public static final int ALGORITHM_BUILD_LIBRARY
```
    See Also:
    
    Constant Field Values
  - TAGS_ALGORITHM
```
public static final weka.core.Tag[] TAGS_ALGORITHM
```
    defines metrics that can be chosen for hillclimbing
- Constructor Detail
  - EnsembleSelection
```
public EnsembleSelection()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - 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.RandomizableClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    We return true for basically everything except for Missing class values, because we can't really answer for all the models in our library. If any of them don't work with the supplied data then we just trap the exception.
    
    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
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Valid options are:
```
 -L </path/to/modelLibrary>
  Specifies the Model Library File, continuing the list of all models.
```
```
 -W </path/to/working/directory>
  Specifies the Working Directory, where all models will be stored.
```
```
 -B <numModelBags>
  Set the number of bags, i.e., number of iterations to run 
  the ensemble selection algorithm.
```
```
 -E <modelRatio>
  Set the ratio of library models that will be randomly chosen 
  to populate each bag of models.
```
```
 -V <validationRatio>
  Set the ratio of the training data set that will be reserved 
  for validation.
```
```
 -H <hillClimbIterations>
  Set the number of hillclimbing iterations to be performed 
  on each model bag.
```
```
 -I <sortInitialization>
  Set the the ratio of the ensemble library that the sort 
  initialization algorithm will be able to choose from while 
  initializing the ensemble for each model bag
```
```
 -X <numFolds>
  Sets the number of cross-validation folds.
```
```
 -P <hillclimbMettric>
  Specify the metric that will be used for model selection 
  during the hillclimbing algorithm.
  Valid metrics are: 
   accuracy, rmse, roc, precision, recall, fscore, all
```
```
 -A <algorithm>
  Specifies the algorithm to be used for ensemble selection. 
  Valid algorithms are:
   "forward" (default) for forward selection.
   "backward" for backward elimination.
   "both" for both forward and backward elimination.
   "best" to simply print out top performer from the 
      ensemble library
   "library" to only train the models in the ensemble 
      library
```
```
 -R
  Flag whether or not models can be selected more than once 
  for an ensemble.
```
```
 -G
  Whether sort initialization greedily stops adding models 
  when performance degrades.
```
```
 -O
  Flag for verbose output. Prints out performance of all 
  selected models.
```
```
 -S <num>
  Random number seed.
  (default 1)
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.RandomizableClassifier
    
    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.RandomizableClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - numFoldsTipText
```
public java.lang.String numFoldsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumFolds
```
public int getNumFolds()
```
    Gets the number of folds for the cross-validation.
    
    Returns:
    
    the number of folds for the cross-validation
  - setNumFolds
```
public void setNumFolds(int numFolds)
                 throws java.lang.Exception
```
    Sets the number of folds for the cross-validation.
    
    Parameters:
    
    numFolds - the number of folds for the cross-validation
    
    Throws:
    
    java.lang.Exception - if parameter illegal
  - libraryTipText
```
public java.lang.String libraryTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getLibrary
```
public EnsembleSelectionLibrary getLibrary()
```
    Gets the ensemble library.
    
    Returns:
    
    the ensemble library
  - setLibrary
```
public void setLibrary(EnsembleSelectionLibrary newLibrary)
```
    Sets the ensemble library.
    
    Parameters:
    
    newLibrary - the ensemble library
  - modelRatioTipText
```
public java.lang.String modelRatioTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getModelRatio
```
public double getModelRatio()
```
    Get the value of modelRatio.
    
    Returns:
    
    Value of modelRatio.
  - setModelRatio
```
public void setModelRatio(double v)
```
    Set the value of modelRatio.
    
    Parameters:
    
    v - Value to assign to modelRatio.
  - validationRatioTipText
```
public java.lang.String validationRatioTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getValidationRatio
```
public double getValidationRatio()
```
    Get the value of validationRatio.
    
    Returns:
    
    Value of validationRatio.
  - setValidationRatio
```
public void setValidationRatio(double v)
```
    Set the value of validationRatio.
    
    Parameters:
    
    v - Value to assign to validationRatio.
  - hillclimbMetricTipText
```
public java.lang.String hillclimbMetricTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getHillclimbMetric
```
public weka.core.SelectedTag getHillclimbMetric()
```
    Gets the hill climbing metric. Will be one of METRIC_ACCURACY, METRIC_RMSE, METRIC_ROC, METRIC_PRECISION, METRIC_RECALL, METRIC_FSCORE, METRIC_ALL
    
    Returns:
    
    the hillclimbMetric
  - setHillclimbMetric
```
public void setHillclimbMetric(weka.core.SelectedTag newType)
```
    Sets the hill climbing metric. Will be one of METRIC_ACCURACY, METRIC_RMSE, METRIC_ROC, METRIC_PRECISION, METRIC_RECALL, METRIC_FSCORE, METRIC_ALL
    
    Parameters:
    
    newType - the new hillclimbMetric
  - algorithmTipText
```
public java.lang.String algorithmTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getAlgorithm
```
public weka.core.SelectedTag getAlgorithm()
```
    Gets the algorithm
    
    Returns:
    
    the algorithm
  - setAlgorithm
```
public void setAlgorithm(weka.core.SelectedTag newType)
```
    Sets the Algorithm to use
    
    Parameters:
    
    newType - the new algorithm
  - hillclimbIterationsTipText
```
public java.lang.String hillclimbIterationsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getHillclimbIterations
```
public int getHillclimbIterations()
```
    Gets the number of hillclimbIterations.
    
    Returns:
    
    the number of hillclimbIterations
  - setHillclimbIterations
```
public void setHillclimbIterations(int n)
                            throws java.lang.Exception
```
    Sets the number of hillclimbIterations.
    
    Parameters:
    
    n - the number of hillclimbIterations
    
    Throws:
    
    java.lang.Exception - if parameter illegal
  - numModelBagsTipText
```
public java.lang.String numModelBagsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumModelBags
```
public int getNumModelBags()
```
    Gets numModelBags.
    
    Returns:
    
    numModelBags
  - setNumModelBags
```
public void setNumModelBags(int n)
                     throws java.lang.Exception
```
    Sets numModelBags.
    
    Parameters:
    
    n - the new value for numModelBags
    
    Throws:
    
    java.lang.Exception - if parameter illegal
  - sortInitializationRatioTipText
```
public java.lang.String sortInitializationRatioTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSortInitializationRatio
```
public double getSortInitializationRatio()
```
    Get the value of sortInitializationRatio.
    
    Returns:
    
    Value of sortInitializationRatio.
  - setSortInitializationRatio
```
public void setSortInitializationRatio(double v)
```
    Set the value of sortInitializationRatio.
    
    Parameters:
    
    v - Value to assign to sortInitializationRatio.
  - replacementTipText
```
public java.lang.String replacementTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getReplacement
```
public boolean getReplacement()
```
    Get the value of replacement.
    
    Returns:
    
    Value of replacement.
  - setReplacement
```
public void setReplacement(boolean newReplacement)
```
    Set the value of replacement.
    
    Parameters:
    
    newReplacement - Value to assign to replacement.
  - greedySortInitializationTipText
```
public java.lang.String greedySortInitializationTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getGreedySortInitialization
```
public boolean getGreedySortInitialization()
```
    Get the value of greedySortInitialization.
    
    Returns:
    
    Value of replacement.
  - setGreedySortInitialization
```
public void setGreedySortInitialization(boolean newGreedySortInitialization)
```
    Set the value of greedySortInitialization.
    
    Parameters:
    
    newGreedySortInitialization - Value to assign to replacement.
  - verboseOutputTipText
```
public java.lang.String verboseOutputTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getVerboseOutput
```
public boolean getVerboseOutput()
```
    Get the value of verboseOutput.
    
    Returns:
    
    Value of verboseOutput.
  - setVerboseOutput
```
public void setVerboseOutput(boolean newVerboseOutput)
```
    Set the value of verboseOutput.
    
    Parameters:
    
    newVerboseOutput - Value to assign to verboseOutput.
  - workingDirectoryTipText
```
public java.lang.String workingDirectoryTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getWorkingDirectory
```
public java.io.File getWorkingDirectory()
```
    Get the value of working directory.
    
    Returns:
    
    Value of working directory.
  - setWorkingDirectory
```
public void setWorkingDirectory(java.io.File newWorkingDirectory)
```
    Set the value of working directory.
    
    Parameters:
    
    newWorkingDirectory - directory Value.
  - buildClassifier
```
public void buildClassifier(weka.core.Instances trainData)
                     throws java.lang.Exception
```
    Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    trainData - 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
  - getDefaultWorkingDirectory
```
public static java.lang.String getDefaultWorkingDirectory()
```
    This method tries to find a reasonable path name for the ensemble working directory where models and files will be stored.
    
    Returns:
    
    true if m_workingDirectory now has a valid file name
  - toString
```
public java.lang.String toString()
```
    Output a representation of this classifier
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the classifier
  - getTechnicalInformation
```
public weka.core.TechnicalInformation getTechnicalInformation()
```
    Return the technical information. There is actually another paper that describes our current method of CV for this classifier TODO: Cite Technical report when published
    
    Specified by:
    
    getTechnicalInformation in interface weka.core.TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - 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)
```
    Executes the classifier from commandline.
    
    Parameters:
    
    argv - should contain the following arguments: -t training file [-T test file] [-c class index]

Class EnsembleSelection

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

TAGS_METRIC

ALGORITHM_FORWARD

ALGORITHM_BACKWARD

ALGORITHM_FORWARD_BACKWARD

ALGORITHM_BEST

ALGORITHM_BUILD_LIBRARY

TAGS_ALGORITHM

Constructor Detail

EnsembleSelection

Method Detail

globalInfo

listOptions

getCapabilities

setOptions

getOptions

numFoldsTipText

getNumFolds

setNumFolds

libraryTipText

getLibrary

setLibrary

modelRatioTipText

getModelRatio

setModelRatio

validationRatioTipText

getValidationRatio

setValidationRatio

hillclimbMetricTipText

getHillclimbMetric

setHillclimbMetric

algorithmTipText

getAlgorithm

setAlgorithm

hillclimbIterationsTipText

getHillclimbIterations

setHillclimbIterations

numModelBagsTipText

getNumModelBags

setNumModelBags

sortInitializationRatioTipText

getSortInitializationRatio

setSortInitializationRatio

replacementTipText

getReplacement

setReplacement

greedySortInitializationTipText

getGreedySortInitialization

setGreedySortInitialization

verboseOutputTipText

getVerboseOutput

setVerboseOutput

workingDirectoryTipText

getWorkingDirectory

setWorkingDirectory

buildClassifier

distributionForInstance

getDefaultWorkingDirectory

toString

getTechnicalInformation

getRevision

main