DecisionTable

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.rules.DecisionTable

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class DecisionTable
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler

Class for building and using a simple decision table majority classifier.

For more information see:

Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.

BibTeX:

 @inproceedings{Kohavi1995,
    author = {Ron Kohavi},
    booktitle = {8th European Conference on Machine Learning},
    pages = {174-189},
    publisher = {Springer},
    title = {The Power of Decision Tables},
    year = {1995}
 }

Valid options are:

 -S <search method specification>
  Full class name of search method, followed
  by its options.
  eg: "weka.attributeSelection.BestFirst -D 1"
  (default weka.attributeSelection.BestFirst)

 -X <number of folds>
  Use cross validation to evaluate features.
  Use number of folds = 1 for leave one out CV.
  (Default = leave one out CV)

 -E <acc | rmse | mae | auc>
  Performance evaluation measure to use for selecting attributes.
  (Default = accuracy for discrete class and rmse for numeric class)

 -I
  Use nearest neighbour instead of global table majority.

 -R
  Display decision table rules.

 Options specific to search method weka.attributeSelection.BestFirst:

 -P <start set>
  Specify a starting set of attributes.
  Eg. 1,3,5-7.

 -D <0 = backward | 1 = forward | 2 = bi-directional>
  Direction of search. (default = 1).

 -N <num>
  Number of non-improving nodes to
  consider before terminating search.

 -S <num>
  Size of lookup cache for evaluated subsets.
  Expressed as a multiple of the number of
  attributes in the data set. (default = 1)

Version:: $Revision: 15519 $
Author:: Mark Hall (mhall@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`EVAL_ACCURACY`
`static int`	`EVAL_AUC`
`static int`	`EVAL_DEFAULT` default is accuracy for discrete class and RMSE for numeric class
`static int`	`EVAL_MAE`
`static int`	`EVAL_RMSE`
`static Tag[]`	`TAGS_EVALUATION`

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

Constructor Summary

Constructors
Constructor and Description

DecisionTable()
Constructor for a DecisionTable

Constructors
Constructor and Description
`DecisionTable()` Constructor for a DecisionTable

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Generates the classifier.
`java.lang.String`	`crossValTipText()` Returns the tip text for this property
`java.lang.String`	`displayRulesTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String`	`evaluationMeasureTipText()` Returns the tip text for this property
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`int`	`getCrossVal()` Gets the number of folds for cross validation
`boolean`	`getDisplayRules()` Gets whether rules are being printed
`SelectedTag`	`getEvaluationMeasure()` Gets the currently set performance evaluation measure used for selecting attributes for the decision table
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`ASSearch`	`getSearch()` Gets the current search method
`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`	`getUseIBk()` Gets whether IBk is being used instead of the majority class
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`double`	`measureNumRules()` Returns the number of rules
`java.lang.String`	`printFeatures()` Returns a string description of the features selected
`java.lang.String`	`searchTipText()` Returns the tip text for this property
`void`	`setCrossVal(int folds)` Sets the number of folds for cross validation (1 = leave one out)
`void`	`setDisplayRules(boolean rules)` Sets whether rules are to be printed
`void`	`setEvaluationMeasure(SelectedTag newMethod)` Sets the performance evaluation measure to use for selecting attributes for the decision table
`void`	`setOptions(java.lang.String[] options)` Parses the options for this object.
`void`	`setSearch(ASSearch search)` Sets the search method to use
`void`	`setUseIBk(boolean ibk)` Sets whether IBk should be used instead of the majority class
`java.lang.String`	`toString()` Returns a description of the classifier.
`java.lang.String`	`useIBkTipText()` Returns the tip text for this property

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
  - EVAL_DEFAULT
```
public static final int EVAL_DEFAULT
```
    default is accuracy for discrete class and RMSE for numeric class
    
    See Also:
    
    Constant Field Values
  - EVAL_ACCURACY
```
public static final int EVAL_ACCURACY
```
    See Also:
    
    Constant Field Values
  - EVAL_RMSE
```
public static final int EVAL_RMSE
```
    See Also:
    
    Constant Field Values
  - EVAL_MAE
```
public static final int EVAL_MAE
```
    See Also:
    
    Constant Field Values
  - EVAL_AUC
```
public static final int EVAL_AUC
```
    See Also:
    
    Constant Field Values
  - TAGS_EVALUATION
```
public static final Tag[] TAGS_EVALUATION
```
- Constructor Detail
  - DecisionTable
```
public DecisionTable()
```
    Constructor for a DecisionTable
- 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
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - crossValTipText
```
public java.lang.String crossValTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCrossVal
```
public void setCrossVal(int folds)
```
    Sets the number of folds for cross validation (1 = leave one out)
    
    Parameters:
    
    folds - the number of folds
  - getCrossVal
```
public int getCrossVal()
```
    Gets the number of folds for cross validation
    
    Returns:
    
    the number of cross validation folds
  - useIBkTipText
```
public java.lang.String useIBkTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseIBk
```
public void setUseIBk(boolean ibk)
```
    Sets whether IBk should be used instead of the majority class
    
    Parameters:
    
    ibk - true if IBk is to be used
  - getUseIBk
```
public boolean getUseIBk()
```
    Gets whether IBk is being used instead of the majority class
    
    Returns:
    
    true if IBk is being used
  - displayRulesTipText
```
public java.lang.String displayRulesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDisplayRules
```
public void setDisplayRules(boolean rules)
```
    Sets whether rules are to be printed
    
    Parameters:
    
    rules - true if rules are to be printed
  - getDisplayRules
```
public boolean getDisplayRules()
```
    Gets whether rules are being printed
    
    Returns:
    
    true if rules are being printed
  - searchTipText
```
public java.lang.String searchTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSearch
```
public void setSearch(ASSearch search)
```
    Sets the search method to use
    
    Parameters:
    
    search -
  - getSearch
```
public ASSearch getSearch()
```
    Gets the current search method
    
    Returns:
    
    the search method used
  - evaluationMeasureTipText
```
public java.lang.String evaluationMeasureTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getEvaluationMeasure
```
public SelectedTag getEvaluationMeasure()
```
    Gets the currently set performance evaluation measure used for selecting attributes for the decision table
    
    Returns:
    
    the performance evaluation measure
  - setEvaluationMeasure
```
public void setEvaluationMeasure(SelectedTag newMethod)
```
    Sets the performance evaluation measure to use for selecting attributes for the decision table
    
    Parameters:
    
    newMethod - the new performance evaluation metric to use
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses the options for this object.
    Valid options are:
```
 -S <search method specification>
  Full class name of search method, followed
  by its options.
  eg: "weka.attributeSelection.BestFirst -D 1"
  (default weka.attributeSelection.BestFirst)
 
```
```
 -X <number of folds>
  Use cross validation to evaluate features.
  Use number of folds = 1 for leave one out CV.
  (Default = leave one out CV)
 
```
```
 -E <acc | rmse | mae | auc>
  Performance evaluation measure to use for selecting attributes.
  (Default = accuracy for discrete class and rmse for numeric class)
 
```
```
 -I
  Use nearest neighbour instead of global table majority.
 
```
```
 -R
  Display decision table rules.
 
```
```
 Options specific to search method weka.attributeSelection.BestFirst:
 
```
```
 -P <start set>
  Specify a starting set of attributes.
  Eg. 1,3,5-7.
 
```
```
 -D <0 = backward | 1 = forward | 2 = bi-directional>
  Direction of search. (default = 1).
 
```
```
 -N <num>
  Number of non-improving nodes to
  consider before terminating search.
 
```
```
 -S <num>
  Size of lookup cache for evaluated subsets.
  Expressed as a multiple of the number of
  attributes in the data set. (default = 1)
 
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class 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 OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    java.lang.Exception - if the classifier has not been generated successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    java.lang.Exception - if distribution can't be computed
  - printFeatures
```
public java.lang.String printFeatures()
```
    Returns a string description of the features selected
    
    Returns:
    
    a string of features
  - measureNumRules
```
public double measureNumRules()
```
    Returns the number of rules
    
    Returns:
    
    the number of rules
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> enumerateMeasures()
```
    Returns an enumeration of the additional measure names
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - 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
  - toString
```
public java.lang.String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a description of the classifier as a string.
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the command-line options

Class DecisionTable

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

EVAL_DEFAULT

EVAL_ACCURACY

EVAL_RMSE

EVAL_MAE

EVAL_AUC

TAGS_EVALUATION

Constructor Detail

DecisionTable

Method Detail

globalInfo

getTechnicalInformation

listOptions

crossValTipText

setCrossVal

getCrossVal

useIBkTipText

setUseIBk

getUseIBk

displayRulesTipText

setDisplayRules

getDisplayRules

searchTipText

setSearch

getSearch

evaluationMeasureTipText

getEvaluationMeasure

setEvaluationMeasure

setOptions

getOptions

getCapabilities

buildClassifier

distributionForInstance

printFeatures

measureNumRules

enumerateMeasures

getMeasure

toString

getRevision

main