OneClassClassifier

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.RandomizableSingleClassifierEnhancer
    - - weka.classifiers.meta.OneClassClassifier

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 OneClassClassifier
extends weka.classifiers.RandomizableSingleClassifierEnhancer
implements weka.core.TechnicalInformationHandler

Performs one-class classification on a dataset.

Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class.

Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes.

For more information, see:

Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.

BibTeX:

 @conference{Hempstalk2008,
    address = {Berlin},
    author = {Kathryn Hempstalk and Eibe Frank and Ian H. Witten},
    booktitle = {Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008},
    month = {September},
    pages = {505--519},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    title = {One-Class Classification by Combining Density and Class Probability Estimation},
    volume = {Vol. 5211},
    year = {2008},
    location = {Antwerp, Belgium}
 }

Valid options are:

 -trr <rate>
  Sets the target rejection rate
  (default: 0.1)

 -tcl <label>
  Sets the target class label
  (default: 'target')

 -cvr <rep>
  Sets the number of times to repeat cross validation
  to find the threshold
  (default: 10)

 -P <prop>
  Sets the proportion of generated data
  (default: 0.5)

 -cvf <perc>
  Sets the percentage of heldout data for each cross validation
  fold
  (default: 10)

 -num <classname + options>
  Sets the numeric generator
  (default: weka.classifiers.meta.generators.GaussianGenerator)

 -nom <classname + options>
  Sets the nominal generator
  (default: weka.classifiers.meta.generators.NominalGenerator)

 -L
  Sets whether to correct the number of classes to two,
  if omitted no correction will be made.

 -E
  Sets whether to exclusively use the density estimate.

 -I
  Sets whether to use instance weights.

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

 -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.meta.Bagging)

 Options specific to classifier weka.classifiers.meta.Bagging:

 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)

 -O
  Calculate the out of bag error.

 -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.REPTree)

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

 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).

 -N <number of folds>
  Number of folds for reduced error pruning (default 3).

 -S <seed>
  Seed for random data shuffling (default 1).

 -P
  No pruning.

 -L
  Maximum tree depth (default -1, no maximum)

Options after -- are passed to the designated classifier.

Version:: $Revision: 10374 $
Author:: Kathryn Hempstalk (kah18 at cs.waikato.ac.nz), Eibe Frank (eibe at cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

static java.lang.String OUTLIER_LABEL
The label for the outlier class.
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Fields
Modifier and Type	Field and Description
`static java.lang.String`	`OUTLIER_LABEL` The label for the outlier class.

Constructor Summary

Constructors
Constructor and Description

OneClassClassifier()
Default constructor.

Constructors
Constructor and Description
`OneClassClassifier()` Default constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances data)` Build the one-class classifier, any non-target data values are ignored.
`java.lang.String`	`densityOnlyTipText()` Returns the tip text for this property.
`double[]`	`distributionForInstance(weka.core.Instance instance)` Returns a probability distribution for a given instance.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the base classifier.
`boolean`	`getDensityOnly()` Gets whether only the density estimate should be used by the classifier.
`NominalAttributeGenerator`	`getNominalGenerator()` Gets the generator that will be used by default to generate nominal outlier data.
`NumericAttributeGenerator`	`getNumericGenerator()` Gets thegenerator that will be used by default to generate numeric outlier data.
`int`	`getNumRepeats()` Gets the number of repeats for (internal) cross validation.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`double`	`getPercentageHeldout()` Gets the percentage of data that will be heldout in each iteration of cross validation.
`double`	`getProportionGenerated()` Gets the proportion of data that will be generated compared to the target class label.
`java.lang.String`	`getRevision()` Returns the revision string.
`java.lang.String`	`getTargetClassLabel()` Gets the target class label - the class label to perform one class classification on.
`double`	`getTargetRejectionRate()` Gets the target rejection rate - the proportion of target class samples that will be rejected in order to build a threshold.
`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`	`getUseInstanceWeights()` Gets whether instance weighting will be performed.
`boolean`	`getUseLaplaceCorrection()` Gets whether a laplace correction should be used.
`java.lang.String`	`globalInfo()` Returns a string describing this classes ability.
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` Main method for executing this classifier.
`java.lang.String`	`nominalGeneratorTipText()` Returns the tip text for this property.
`java.lang.String`	`numericGeneratorTipText()` Returns the tip text for this property.
`java.lang.String`	`numRepeatsTipText()` Returns the tip text for this property.
`java.lang.String`	`percentageHeldoutTipText()` Returns the tip text for this property.
`java.lang.String`	`proportionGeneratedTipText()` Returns the tip text for this property.
`void`	`setDensityOnly(boolean density)` Sets whether the density estimate will be used by itself.
`void`	`setNominalGenerator(NominalAttributeGenerator agen)` Sets the generator that will be used by default to generate nominal outlier data.
`void`	`setNumericGenerator(NumericAttributeGenerator agen)` Sets the generator that will be used by default to generate numeric outlier data.
`void`	`setNumRepeats(int repeats)` Sets the number of repeats for (internal) cross validation to a new value.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPercentageHeldout(double percent)` Sets the percentage heldout in each CV fold.
`void`	`setProportionGenerated(double prop)` Sets the proportion of generated data to a new value.
`void`	`setTargetClassLabel(java.lang.String label)` Sets the target class label to a new value.
`void`	`setTargetRejectionRate(double rate)` Sets the target rejection rate.
`void`	`setUseInstanceWeights(boolean newuse)` Sets whether to perform weighting on instances based on their prevalence in the data.
`void`	`setUseLaplaceCorrection(boolean newuse)` Sets whether a laplace correction should be used.
`java.lang.String`	`targetClassLabelTipText()` Returns the tip text for this property.
`java.lang.String`	`targetRejectionRateTipText()` Returns the tip text for this property.
`java.lang.String`	`toString()` Output a representation of this classifier
`java.lang.String`	`useInstanceWeightsTipText()` Returns the tip text for this property.
`java.lang.String`	`useLaplaceCorrectionTipText()` Returns the tip text for this property.

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

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

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, 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
  - OUTLIER_LABEL
```
public static final java.lang.String OUTLIER_LABEL
```
    The label for the outlier class.
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - OneClassClassifier
```
public OneClassClassifier()
```
    Default constructor.
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this classes ability.
    
    Returns:
    
    A description of the method.
  - 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.RandomizableSingleClassifierEnhancer
    
    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:
```
 -trr <rate>
  Sets the target rejection rate
  (default: 0.1)
 
```
```
 -tcl <label>
  Sets the target class label
  (default: 'target')
 
```
```
 -cvr <rep>
  Sets the number of times to repeat cross validation
  to find the threshold
  (default: 10)
 
```
```
 -P <prop>
  Sets the proportion of generated data
  (default: 0.5)
 
```
```
 -cvf <perc>
  Sets the percentage of heldout data for each cross validation
  fold
  (default: 10)
 
```
```
 -num <classname + options>
  Sets the numeric generator
  (default: weka.classifiers.meta.generators.GaussianGenerator)
 
```
```
 -nom <classname + options>
  Sets the nominal generator
  (default: weka.classifiers.meta.generators.NominalGenerator)
 
```
```
 -L
  Sets whether to correct the number of classes to two,
  if omitted no correction will be made.
 
```
```
 -E
  Sets whether to exclusively use the density estimate.
 
```
```
 -I
  Sets whether to use instance weights.
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.meta.Bagging)
 
```
```
 Options specific to classifier weka.classifiers.meta.Bagging:
 
```
```
 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)
 
```
```
 -O
  Calculate the out of bag error.
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -I <num>
  Number of iterations.
  (default 10)
 
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.REPTree)
 
```
```
 Options specific to classifier weka.classifiers.trees.REPTree:
 
```
```
 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).
 
```
```
 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
 
```
```
 -N <number of folds>
  Number of folds for reduced error pruning (default 3).
 
```
```
 -S <seed>
  Seed for random data shuffling (default 1).
 
```
```
 -P
  No pruning.
 
```
```
 -L
  Maximum tree depth (default -1, no maximum)
 
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.RandomizableSingleClassifierEnhancer
    
    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.RandomizableSingleClassifierEnhancer
    
    Returns:
    
    An array of strings suitable for passing to setOptions.
  - getDensityOnly
```
public boolean getDensityOnly()
```
    Gets whether only the density estimate should be used by the classifier. If false, the base classifier's estimate will be incorporated using bayes rule for two classes.
    
    Returns:
    
    Whether to use only the density estimate.
  - setDensityOnly
```
public void setDensityOnly(boolean density)
```
    Sets whether the density estimate will be used by itself.
    
    Parameters:
    
    density - Whether to use the density estimate exclusively or not.
  - densityOnlyTipText
```
public java.lang.String densityOnlyTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getTargetRejectionRate
```
public double getTargetRejectionRate()
```
    Gets the target rejection rate - the proportion of target class samples that will be rejected in order to build a threshold.
    
    Returns:
    
    The target rejection rate.
  - setTargetRejectionRate
```
public void setTargetRejectionRate(double rate)
```
    Sets the target rejection rate.
    
    Parameters:
    
    rate - The new target rejection rate.
  - targetRejectionRateTipText
```
public java.lang.String targetRejectionRateTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getTargetClassLabel
```
public java.lang.String getTargetClassLabel()
```
    Gets the target class label - the class label to perform one class classification on.
    
    Returns:
    
    The target class label.
  - setTargetClassLabel
```
public void setTargetClassLabel(java.lang.String label)
```
    Sets the target class label to a new value.
    
    Parameters:
    
    label - The target class label to classify for.
  - targetClassLabelTipText
```
public java.lang.String targetClassLabelTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumRepeats
```
public int getNumRepeats()
```
    Gets the number of repeats for (internal) cross validation.
    
    Returns:
    
    The number of repeats for internal cross validation.
  - setNumRepeats
```
public void setNumRepeats(int repeats)
```
    Sets the number of repeats for (internal) cross validation to a new value.
    
    Parameters:
    
    repeats - The new number of repeats for cross validation.
  - numRepeatsTipText
```
public java.lang.String numRepeatsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setProportionGenerated
```
public void setProportionGenerated(double prop)
```
    Sets the proportion of generated data to a new value.
    
    Parameters:
    
    prop - The new proportion.
  - getProportionGenerated
```
public double getProportionGenerated()
```
    Gets the proportion of data that will be generated compared to the target class label.
    
    Returns:
    
    The proportion of generated data.
  - proportionGeneratedTipText
```
public java.lang.String proportionGeneratedTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPercentageHeldout
```
public void setPercentageHeldout(double percent)
```
    Sets the percentage heldout in each CV fold.
    
    Parameters:
    
    percent - The new percent of heldout data.
  - getPercentageHeldout
```
public double getPercentageHeldout()
```
    Gets the percentage of data that will be heldout in each iteration of cross validation.
    
    Returns:
    
    The percentage of heldout data.
  - percentageHeldoutTipText
```
public java.lang.String percentageHeldoutTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumericGenerator
```
public NumericAttributeGenerator getNumericGenerator()
```
    Gets thegenerator that will be used by default to generate numeric outlier data.
    
    Returns:
    
    The numeric data generator.
  - setNumericGenerator
```
public void setNumericGenerator(NumericAttributeGenerator agen)
```
    Sets the generator that will be used by default to generate numeric outlier data.
    
    Parameters:
    
    agen - The new numeric data generator to use.
  - numericGeneratorTipText
```
public java.lang.String numericGeneratorTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNominalGenerator
```
public NominalAttributeGenerator getNominalGenerator()
```
    Gets the generator that will be used by default to generate nominal outlier data.
    
    Returns:
    
    The nominal data generator.
  - setNominalGenerator
```
public void setNominalGenerator(NominalAttributeGenerator agen)
```
    Sets the generator that will be used by default to generate nominal outlier data.
    
    Parameters:
    
    agen - The new nominal data generator to use.
  - nominalGeneratorTipText
```
public java.lang.String nominalGeneratorTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getUseLaplaceCorrection
```
public boolean getUseLaplaceCorrection()
```
    Gets whether a laplace correction should be used.
    
    Returns:
    
    Whether a laplace correction should be used.
  - setUseLaplaceCorrection
```
public void setUseLaplaceCorrection(boolean newuse)
```
    Sets whether a laplace correction should be used. A laplace correction will reduce the number of class labels to two, the target and outlier classes, regardless of how many labels actually exist. This is useful for classifiers that use the number of class labels to make use a laplace value based on the unseen class.
    
    Parameters:
    
    newuse - Whether to use the laplace correction (default: true).
  - useLaplaceCorrectionTipText
```
public java.lang.String useLaplaceCorrectionTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseInstanceWeights
```
public void setUseInstanceWeights(boolean newuse)
```
    Sets whether to perform weighting on instances based on their prevalence in the data.
    
    Parameters:
    
    newuse - Whether or not to use instance weighting.
  - getUseInstanceWeights
```
public boolean getUseInstanceWeights()
```
    Gets whether instance weighting will be performed.
    
    Returns:
    
    Whether instance weighting will be performed.
  - useInstanceWeightsTipText
```
public java.lang.String useInstanceWeightsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the base 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 the base classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception
```
    Build the one-class classifier, any non-target data values are ignored. The target class label must exist in the arff file or else an exception will be thrown.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    data - The training data.
    
    Throws:
    
    java.lang.Exception - If the classifier could not be built successfully.
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception
```
    Returns a probability distribution for a given instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - The instance to calculate the probability distribution for.
    
    Returns:
    
    The probability for each class.
    
    Throws:
    
    java.lang.Exception
  - toString
```
public java.lang.String toString()
```
    Output a representation of this classifier
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a representation of this classifier
  - 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 string.
  - main
```
public static void main(java.lang.String[] args)
```
    Main method for executing this classifier.
    
    Parameters:
    
    args - use -h to see all available options

Class OneClassClassifier

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

OUTLIER_LABEL

Constructor Detail

OneClassClassifier

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

getDensityOnly

setDensityOnly

densityOnlyTipText

getTargetRejectionRate

setTargetRejectionRate

targetRejectionRateTipText

getTargetClassLabel

setTargetClassLabel

targetClassLabelTipText

getNumRepeats

setNumRepeats

numRepeatsTipText

setProportionGenerated

getProportionGenerated

proportionGeneratedTipText

setPercentageHeldout

getPercentageHeldout

percentageHeldoutTipText

getNumericGenerator

setNumericGenerator

numericGeneratorTipText

getNominalGenerator

setNominalGenerator

nominalGeneratorTipText

getUseLaplaceCorrection

setUseLaplaceCorrection

useLaplaceCorrectionTipText

setUseInstanceWeights

getUseInstanceWeights

useInstanceWeightsTipText

getCapabilities

buildClassifier

distributionForInstance

toString

getRevision

main