TLC

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.mi.TLC

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

public class TLC
extends weka.classifiers.SingleClassifierEnhancer
implements weka.core.TechnicalInformationHandler, weka.core.MultiInstanceCapabilitiesHandler

Implements basic two-level classification method for multi-instance data, without attribute selection.

For more information see:

Nils Weidmann, Eibe Frank, Bernhard Pfahringer: A two-level learning method for generalized multi-instance problems. In: Fourteenth European Conference on Machine Learning, 468-479, 2003.
Eibe Frank and Bernhard Pfahringer: Propositionalisation of Multi-instance Data Using Random Forests. In: AI 2013: Advances in Artificial Intelligence, 362-373, 2013.

BibTeX:

 @inproceedings{Weidmann2003,
    author = {Nils Weidmann and Eibe Frank and Bernhard Pfahringer},
    booktitle = {Fourteenth European Conference on Machine Learning},
    pages = {468-479},
    publisher = {Springer},
    title = {A two-level learning method for generalized multi-instance problems},
    year = {2003}
 }

 @inproceedings{FrankAndPfahringer203,
    author = {Eibe Frank and Bernhard Pfahringer},
    booktitle = {AI 2013: Advances in Artificial Intelligence},
    pages = {362-373},
    publisher = {Springer},
    title = {Propositionalisation of Multi-instance Data Using Random Forests},
    year = {2013}
 }

Valid options are:

 -P "<name and options of partition generator>"
  Partition generator to use, including options.
  Quotes are needed when options are specified.
  (default: weka.classifiers.trees.J48)

 -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.LogitBoost)

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

 -Q
  Use resampling instead of reweighting for boosting.

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

 -F <num>
  Number of folds for internal cross-validation.
  (default 0 -- no cross-validation)

 -R <num>
  Number of runs for internal cross-validation.
  (default 1)

 -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)

 -H <num>
  Shrinkage parameter.
  (default 1)

 -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 specific to partition generator weka.classifiers.trees.J48:

 -U
  Use unpruned tree.

 -O
  Do not collapse tree.

 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)

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

 -R
  Use reduced error pruning.

 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)

 -B
  Use binary splits only.

 -S
  Don't perform subtree raising.

 -L
  Do not clean up after the tree has been built.

 -A
  Laplace smoothing for predicted probabilities.

 -J
  Do not use MDL correction for info gain on numeric attributes.

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

Version:: $Revision: 14016 $
Author:: Eibe Frank (eibe@.cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

TLC()
Constructor that sets default base learner.

Constructors
Constructor and Description
`TLC()` Constructor that sets default base learner.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances data)` Builds the classifier from the given training data.
`double[]`	`distributionForInstance(weka.core.Instance inst)` Returns class probabilities for the given instance.
`weka.core.Capabilities`	`getCapabilities()` Returns the Capabilities of this filter.
`weka.core.Capabilities`	`getMultiInstanceCapabilities()` Returns the capabilities of this multi-instance filter for the relational data (i.e., the bags).
`java.lang.String[]`	`getOptions()` Gets the current settings of the filter.
`weka.core.PartitionGenerator`	`getPartitionGenerator()` Get the generator used by this filter
`java.lang.String`	`getRevision()` Returns the revision string.
`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[] options)` Main method for running this class from the command-line.
`java.lang.String`	`partitionGeneratorTipText()` Returns a description of this option suitable for display as a tip text in the gui.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPartitionGenerator(weka.core.PartitionGenerator newPartitionGenerator)` Set the generator for use in filtering
`java.lang.String`	`toString()` Returns a description of the classifier as a string.

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

- Constructor Detail
  - TLC
```
public TLC()
```
    Constructor that sets default base learner.
- 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
  - partitionGeneratorTipText
```
public java.lang.String partitionGeneratorTipText()
```
    Returns a description of this option suitable for display as a tip text in the gui.
    
    Returns:
    
    description of this option
  - setPartitionGenerator
```
public void setPartitionGenerator(weka.core.PartitionGenerator newPartitionGenerator)
```
    Set the generator for use in filtering
    
    Parameters:
    
    newPartitionGenerator - the generator to use
  - getPartitionGenerator
```
public weka.core.PartitionGenerator getPartitionGenerator()
```
    Get the generator used by this filter
    
    Returns:
    
    the generator used
  - 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.SingleClassifierEnhancer
    
    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:
```
 -P "<name and options of partition generator>"
  Partition generator to use, including options.
  Quotes are needed when options are specified.
  (default: weka.classifiers.trees.J48)
 
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.meta.LogitBoost)
 
```
```
 Options specific to classifier weka.classifiers.meta.LogitBoost:
 
```
```
 -Q
  Use resampling instead of reweighting for boosting.
 
```
```
 -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 
```
```
 -F <num>
  Number of folds for internal cross-validation.
  (default 0 -- no cross-validation)
 
```
```
 -R <num>
  Number of runs for internal cross-validation.
  (default 1)
 
```
```
 -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)
 
```
```
 -H <num>
  Shrinkage parameter.
  (default 1)
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -I <num>
  Number of iterations.
  (default 10)
 
```
```
 -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 specific to partition generator weka.classifiers.trees.J48:
 
```
```
 -U
  Use unpruned tree.
 
```
```
 -O
  Do not collapse tree.
 
```
```
 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
 
```
```
 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
 
```
```
 -R
  Use reduced error pruning.
 
```
```
 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
 
```
```
 -B
  Use binary splits only.
 
```
```
 -S
  Don't perform subtree raising.
 
```
```
 -L
  Do not clean up after the tree has been built.
 
```
```
 -A
  Laplace smoothing for predicted probabilities.
 
```
```
 -J
  Do not use MDL correction for info gain on numeric attributes.
 
```
```
 -Q <seed>
  Seed for random data shuffling (default 1).
 
```
    Options after the -- are passed on to the clusterer.
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.SingleClassifierEnhancer
    
    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 filter.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.SingleClassifierEnhancer
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception
```
    Builds the classifier from the given training data.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Throws:
    
    java.lang.Exception
  - toString
```
public java.lang.String toString()
```
    Returns a description of the classifier as a string.
    
    Overrides:
    
    toString in class java.lang.Object
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance inst)
                                 throws java.lang.Exception
```
    Returns class probabilities for the given instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Throws:
    
    java.lang.Exception
  - 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
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns the Capabilities of this filter.
    
    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 object
    
    See Also:
    
    Capabilities
  - getMultiInstanceCapabilities
```
public weka.core.Capabilities getMultiInstanceCapabilities()
```
    Returns the capabilities of this multi-instance filter for the relational data (i.e., the bags).
    
    Specified by:
    
    getMultiInstanceCapabilities in interface weka.core.MultiInstanceCapabilitiesHandler
    
    Returns:
    
    the capabilities of this object
    
    See Also:
    
    Capabilities
  - main
```
public static void main(java.lang.String[] options)
```
    Main method for running this class from the command-line.

Class TLC

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

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

Constructor Detail

TLC

Method Detail

globalInfo

getTechnicalInformation

partitionGeneratorTipText

setPartitionGenerator

getPartitionGenerator

listOptions

setOptions

getOptions

buildClassifier

toString

distributionForInstance

getRevision

getCapabilities

getMultiInstanceCapabilities

main