BFTree

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.RandomizableClassifier
  - - weka.classifiers.trees.BFTree

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler

public class BFTree
extends RandomizableClassifier
implements AdditionalMeasureProducer, TechnicalInformationHandler

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used.

For more information, see:

Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ.

Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

BibTeX:

 @mastersthesis{Shi2007,
    address = {Hamilton, NZ},
    author = {Haijian Shi},
    note = {COMP594},
    school = {University of Waikato},
    title = {Best-first decision tree learning},
    year = {2007}
 }
 
 @article{Friedman2000,
    author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    journal = {Annals of statistics},
    number = {2},
    pages = {337-407},
    title = {Additive logistic regression : A statistical view of boosting},
    volume = {28},
    year = {2000},
    ISSN = {0090-5364}
 }

Valid options are:

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

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

 -P <UNPRUNED|POSTPRUNED|PREPRUNED>
  The pruning strategy.
  (default: POSTPRUNED)

 -M <min no>
  The minimal number of instances at the terminal nodes.
  (default 2)

 -N <num folds>
  The number of folds used in the pruning.
  (default 5)

 -H
  Don't use heuristic search for nominal attributes in multi-class
  problem (default yes).

 -G
  Don't use Gini index for splitting (default yes),
  if not information is used.

 -R
  Don't use error rate in internal cross-validation (default yes), 
  but root mean squared error.

 -A
  Use the 1 SE rule to make pruning decision.
  (default no).

 -C
  Percentage of training data size (0-1]
  (default 1).

Version:: $Revision: 6947 $
Author:: Haijian Shi (hs69@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`PRUNING_POSTPRUNING` pruning strategy: post-pruning
`static int`	`PRUNING_PREPRUNING` pruning strategy: pre-pruning
`static int`	`PRUNING_UNPRUNED` pruning strategy: un-pruned
`static Tag[]`	`TAGS_PRUNING` pruning strategy

Constructor Summary

Constructors
Constructor and Description

BFTree()

Constructors
Constructor and Description
`BFTree()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Method for building a BestFirst decision tree classifier.
`double[]`	`distributionForInstance(Instance instance)` Computes class probabilities for instance using the decision tree.
`java.util.Enumeration`	`enumerateMeasures()` Return an enumeration of the measure names.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getHeuristic()` Get if use heuristic search for nominal attributes in multi-class problems.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`int`	`getMinNumObj()` Get minimal number of instances at the terminal nodes.
`int`	`getNumFoldsPruning()` Set number of folds in internal cross-validation.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`SelectedTag`	`getPruningStrategy()` Gets the pruning strategy.
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSizePer()` Get training set size.
`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`	`getUseErrorRate()` Get if use error rate in internal cross-validation.
`boolean`	`getUseGini()` Get if use Gini index as splitting criterion.
`boolean`	`getUseOneSE()` Get if use the 1SE rule to choose final model.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.lang.String`	`heuristicTipText()` Returns the tip text for this property
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` Main method.
`double`	`measureTreeSize()` Return number of tree size.
`java.lang.String`	`minNumObjTipText()` Returns the tip text for this property
`java.lang.String`	`numFoldsPruningTipText()` Returns the tip text for this property
`int`	`numLeaves()` Compute number of leaf nodes.
`int`	`numNodes()` Compute size of the tree.
`java.lang.String`	`pruningStrategyTipText()` Returns the tip text for this property
`void`	`setHeuristic(boolean value)` Set if use heuristic search for nominal attributes in multi-class problems.
`void`	`setMinNumObj(int value)` Set minimal number of instances at the terminal nodes.
`void`	`setNumFoldsPruning(int value)` Set number of folds in internal cross-validation.
`void`	`setOptions(java.lang.String[] options)` Parses the options for this object.
`void`	`setPruningStrategy(SelectedTag value)` Sets the pruning strategy.
`void`	`setSizePer(double value)` Set training set size.
`void`	`setUseErrorRate(boolean value)` Set if use error rate in internal cross-validation.
`void`	`setUseGini(boolean value)` Set if use Gini index as splitting criterion.
`void`	`setUseOneSE(boolean value)` Set if use the 1SE rule to choose final model.
`java.lang.String`	`sizePerTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Prints the decision tree using the protected toString method from below.
`java.lang.String`	`useErrorRateTipText()` Returns the tip text for this property
`java.lang.String`	`useGiniTipText()` Returns the tip text for this property
`java.lang.String`	`useOneSETipText()` Returns the tip text for this property

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

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

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

- Field Detail
  - PRUNING_UNPRUNED
```
public static final int PRUNING_UNPRUNED
```
    pruning strategy: un-pruned
    
    See Also:
    
    Constant Field Values
  - PRUNING_POSTPRUNING
```
public static final int PRUNING_POSTPRUNING
```
    pruning strategy: post-pruning
    
    See Also:
    
    Constant Field Values
  - PRUNING_PREPRUNING
```
public static final int PRUNING_PREPRUNING
```
    pruning strategy: pre-pruning
    
    See Also:
    
    Constant Field Values
  - TAGS_PRUNING
```
public static final Tag[] TAGS_PRUNING
```
    pruning strategy
- Constructor Detail
  - BFTree
```
public BFTree()
```
- 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 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 TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Method for building a BestFirst decision tree classifier.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if decision tree cannot be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Computes class probabilities for instance using the decision tree.
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    instance - the instance for which class probabilities is to be computed
    
    Returns:
    
    the class probabilities for the given instance
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - toString
```
public java.lang.String toString()
```
    Prints the decision tree using the protected toString method from below.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a textual description of the classifier
  - numNodes
```
public int numNodes()
```
    Compute size of the tree.
    
    Returns:
    
    size of the tree
  - numLeaves
```
public int numLeaves()
```
    Compute number of leaf nodes.
    
    Returns:
    
    number of leaf nodes
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableClassifier
    
    Returns:
    
    an enumeration describing the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses the options for this object.
    Valid options are:
```
 -S <num>
  Random number seed.
  (default 1)
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
```
 -P <UNPRUNED|POSTPRUNED|PREPRUNED>
  The pruning strategy.
  (default: POSTPRUNED)
```
```
 -M <min no>
  The minimal number of instances at the terminal nodes.
  (default 2)
```
```
 -N <num folds>
  The number of folds used in the pruning.
  (default 5)
```
```
 -H
  Don't use heuristic search for nominal attributes in multi-class
  problem (default yes).
 
```
```
 -G
  Don't use Gini index for splitting (default yes),
  if not information is used.
```
```
 -R
  Don't use error rate in internal cross-validation (default yes), 
  but root mean squared error.
```
```
 -A
  Use the 1 SE rule to make pruning decision.
  (default no).
```
```
 -C
  Percentage of training data size (0-1]
  (default 1).
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableClassifier
    
    Parameters:
    
    options - the options to use
    
    Throws:
    
    java.lang.Exception - if setting of options fails
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableClassifier
    
    Returns:
    
    the current settings of the Classifier
  - enumerateMeasures
```
public java.util.Enumeration enumerateMeasures()
```
    Return an enumeration of the measure names.
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - measureTreeSize
```
public double measureTreeSize()
```
    Return number of tree size.
    
    Returns:
    
    number of tree size
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - pruningStrategyTipText
```
public java.lang.String pruningStrategyTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPruningStrategy
```
public void setPruningStrategy(SelectedTag value)
```
    Sets the pruning strategy.
    
    Parameters:
    
    value - the strategy
  - getPruningStrategy
```
public SelectedTag getPruningStrategy()
```
    Gets the pruning strategy.
    
    Returns:
    
    the current strategy.
  - minNumObjTipText
```
public java.lang.String minNumObjTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinNumObj
```
public void setMinNumObj(int value)
```
    Set minimal number of instances at the terminal nodes.
    
    Parameters:
    
    value - minimal number of instances at the terminal nodes
  - getMinNumObj
```
public int getMinNumObj()
```
    Get minimal number of instances at the terminal nodes.
    
    Returns:
    
    minimal number of instances at the terminal nodes
  - numFoldsPruningTipText
```
public java.lang.String numFoldsPruningTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumFoldsPruning
```
public void setNumFoldsPruning(int value)
```
    Set number of folds in internal cross-validation.
    
    Parameters:
    
    value - the number of folds
  - getNumFoldsPruning
```
public int getNumFoldsPruning()
```
    Set number of folds in internal cross-validation.
    
    Returns:
    
    number of folds in internal cross-validation
  - heuristicTipText
```
public java.lang.String heuristicTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui.
  - setHeuristic
```
public void setHeuristic(boolean value)
```
    Set if use heuristic search for nominal attributes in multi-class problems.
    
    Parameters:
    
    value - if use heuristic search for nominal attributes in multi-class problems
  - getHeuristic
```
public boolean getHeuristic()
```
    Get if use heuristic search for nominal attributes in multi-class problems.
    
    Returns:
    
    if use heuristic search for nominal attributes in multi-class problems
  - useGiniTipText
```
public java.lang.String useGiniTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui.
  - setUseGini
```
public void setUseGini(boolean value)
```
    Set if use Gini index as splitting criterion.
    
    Parameters:
    
    value - if use Gini index splitting criterion
  - getUseGini
```
public boolean getUseGini()
```
    Get if use Gini index as splitting criterion.
    
    Returns:
    
    if use Gini index as splitting criterion
  - useErrorRateTipText
```
public java.lang.String useErrorRateTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui.
  - setUseErrorRate
```
public void setUseErrorRate(boolean value)
```
    Set if use error rate in internal cross-validation.
    
    Parameters:
    
    value - if use error rate in internal cross-validation
  - getUseErrorRate
```
public boolean getUseErrorRate()
```
    Get if use error rate in internal cross-validation.
    
    Returns:
    
    if use error rate in internal cross-validation.
  - useOneSETipText
```
public java.lang.String useOneSETipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui.
  - setUseOneSE
```
public void setUseOneSE(boolean value)
```
    Set if use the 1SE rule to choose final model.
    
    Parameters:
    
    value - if use the 1SE rule to choose final model
  - getUseOneSE
```
public boolean getUseOneSE()
```
    Get if use the 1SE rule to choose final model.
    
    Returns:
    
    if use the 1SE rule to choose final model
  - sizePerTipText
```
public java.lang.String sizePerTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui.
  - setSizePer
```
public void setSizePer(double value)
```
    Set training set size.
    
    Parameters:
    
    value - training set size
  - getSizePer
```
public double getSizePer()
```
    Get training set size.
    
    Returns:
    
    training set size
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method.
    
    Parameters:
    
    args - the options for the classifier

Class BFTree

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

PRUNING_UNPRUNED

PRUNING_POSTPRUNING

PRUNING_PREPRUNING

TAGS_PRUNING

Constructor Detail

BFTree

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

distributionForInstance

toString

numNodes

numLeaves

listOptions

setOptions

getOptions

enumerateMeasures

measureTreeSize

getMeasure

pruningStrategyTipText

setPruningStrategy

getPruningStrategy

minNumObjTipText

setMinNumObj

getMinNumObj

numFoldsPruningTipText

setNumFoldsPruning

getNumFoldsPruning

heuristicTipText

setHeuristic

getHeuristic

useGiniTipText

setUseGini

getUseGini

useErrorRateTipText

setUseErrorRate

getUseErrorRate

useOneSETipText

setUseOneSE

getUseOneSE

sizePerTipText

setSizePer

getSizePer

getRevision

main