DecisionTable (weka-stable 3.6.8 API)

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

All Implemented Interfaces:: Serializable, Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

Direct Known Subclasses:: DTNB

public class DecisionTable
extends Classifier
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: 5981 $
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`
`protected boolean`	`m_classIsNominal` Class is nominal
`protected double[]`	`m_classPriorCounts` The class priors to use when there is no match in the table
`protected double[]`	`m_classPriors`
`protected int[]`	`m_decisionFeatures` Holds the final feature set
`protected Remove`	`m_delTransform` Filter used to remove columns discarded by feature selection
`protected boolean`	`m_displayRules` Display Rules
`protected Filter`	`m_disTransform` Discretization filter
`protected Instances`	`m_dtInstances` Holds the final feature selected set of instances
`protected Hashtable`	`m_entries` The hashtable used to hold training instances
`protected Evaluation`	`m_evaluation` The evaluation object used to evaluate subsets
`protected int`	`m_evaluationMeasure`
`protected ASEvaluation`	`m_evaluator` Our own internal evaluator
`protected IBk`	`m_ibk` IB1 used to classify non matching instances rather than majority class
`protected double`	`m_majority` Holds the majority class
`protected int`	`m_numAttributes` The number of attributes in the dataset
`protected boolean`	`m_saveMemory`
`protected ASSearch`	`m_search` The search method to use
`protected Instances`	`m_theInstances` Holds the original training instances
`protected boolean`	`m_useIBk` Use the IBk classifier rather than majority class
`static Tag[]`	`TAGS_EVALUATION`

Fields inherited from class weka.classifiers.Classifier
m_Debug

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.
`String`	`crossValTipText()` Returns the tip text for this property
`String`	`displayRulesTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`protected double`	`estimatePerformance(BitSet feature_set, int num_atts)` Evaluates a feature subset by cross validation
`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(String additionalMeasureName)` Returns the value of the named measure
`String[]`	`getOptions()` Gets the current settings of the classifier.
`String`	`getRevision()` Returns the revision string.
`ASSearch`	`getSearch()` Gets the current search method
`protected String`	`getSearchSpec()` Gets the search specification string, which contains the class name of the search method and any options to it
`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
`String`	`globalInfo()` Returns a string describing classifier
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class.
`double`	`measureNumRules()` Returns the number of rules
`String`	`printFeatures()` Returns a string description of the features selected
`protected void`	`resetOptions()` Resets the options.
`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(String[] options)` Parses the options for this object.
`void`	`setSearch(ASSearch search)` Sets the search method to use
`protected void`	`setUpEvaluator()` Sets up a dummy subset evaluator that basically just delegates evaluation to the estimatePerformance method in DecisionTable
`void`	`setUseIBk(boolean ibk)` Sets whether IBk should be used instead of the majority class
`String`	`toString()` Returns a description of the classifier.
`String`	`useIBkTipText()` Returns the tip text for this property

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

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

- Field Detail
  - m_entries
```
protected Hashtable m_entries
```
    The hashtable used to hold training instances
  - m_classPriorCounts
```
protected double[] m_classPriorCounts
```
    The class priors to use when there is no match in the table
  - m_classPriors
```
protected double[] m_classPriors
```
  - m_decisionFeatures
```
protected int[] m_decisionFeatures
```
    Holds the final feature set
  - m_disTransform
```
protected Filter m_disTransform
```
    Discretization filter
  - m_delTransform
```
protected Remove m_delTransform
```
    Filter used to remove columns discarded by feature selection
  - m_ibk
```
protected IBk m_ibk
```
    IB1 used to classify non matching instances rather than majority class
  - m_theInstances
```
protected Instances m_theInstances
```
    Holds the original training instances
  - m_dtInstances
```
protected Instances m_dtInstances
```
    Holds the final feature selected set of instances
  - m_numAttributes
```
protected int m_numAttributes
```
    The number of attributes in the dataset
  - m_classIsNominal
```
protected boolean m_classIsNominal
```
    Class is nominal
  - m_useIBk
```
protected boolean m_useIBk
```
    Use the IBk classifier rather than majority class
  - m_displayRules
```
protected boolean m_displayRules
```
    Display Rules
  - m_majority
```
protected double m_majority
```
    Holds the majority class
  - m_search
```
protected ASSearch m_search
```
    The search method to use
  - m_evaluator
```
protected ASEvaluation m_evaluator
```
    Our own internal evaluator
  - m_evaluation
```
protected Evaluation m_evaluation
```
    The evaluation object used to evaluate subsets
  - 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
```
  - m_evaluationMeasure
```
protected int m_evaluationMeasure
```
  - m_saveMemory
```
protected boolean m_saveMemory
```
- Constructor Detail
  - DecisionTable
```
public DecisionTable()
```
    Constructor for a DecisionTable
- Method Detail
  - globalInfo
```
public 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
  - estimatePerformance
```
protected double estimatePerformance(BitSet feature_set,
                                     int num_atts)
                              throws Exception
```
    Evaluates a feature subset by cross validation
    
    Parameters:
    
    feature_set - the subset to be evaluated
    
    num_atts - the number of attributes in the subset
    
    Returns:
    
    the estimated accuracy
    
    Throws:
    
    Exception - if subset can't be evaluated
  - resetOptions
```
protected void resetOptions()
```
    Resets the options.
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class Classifier
    
    Returns:
    
    an enumeration of all the available options.
  - crossValTipText
```
public 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 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 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 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 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(String[] options)
                throws 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 Classifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - getSearchSpec
```
protected String getSearchSpec()
```
    Gets the search specification string, which contains the class name of the search method and any options to it
    
    Returns:
    
    the search string.
  - 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
  - setUpEvaluator
```
protected void setUpEvaluator()
                       throws Exception
```
    Sets up a dummy subset evaluator that basically just delegates evaluation to the estimatePerformance method in DecisionTable
    
    Throws:
    
    Exception
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws Exception
```
    Generates the classifier.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    data - set of instances serving as training data
    
    Throws:
    
    Exception - if the classifier has not been generated successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    Exception - if distribution can't be computed
  - printFeatures
```
public 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 Enumeration 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(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:
    
    IllegalArgumentException - if the named measure is not supported
  - toString
```
public String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a description of the classifier as a string.
  - getRevision
```
public 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(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the command-line options

Class DecisionTable

Field Summary

Fields inherited from class weka.classifiers.Classifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

m_entries

m_classPriorCounts

m_classPriors

m_decisionFeatures

m_disTransform

m_delTransform

m_ibk

m_theInstances

m_dtInstances

m_numAttributes

m_classIsNominal

m_useIBk

m_displayRules

m_majority

m_search

m_evaluator

m_evaluation

EVAL_DEFAULT

EVAL_ACCURACY

EVAL_RMSE

EVAL_MAE

EVAL_AUC

TAGS_EVALUATION

m_evaluationMeasure

m_saveMemory

Constructor Detail

DecisionTable

Method Detail

globalInfo

getTechnicalInformation

estimatePerformance

resetOptions

listOptions

crossValTipText

setCrossVal

getCrossVal

useIBkTipText

setUseIBk

getUseIBk

displayRulesTipText

setDisplayRules

getDisplayRules

searchTipText

setSearch

getSearch

evaluationMeasureTipText

getEvaluationMeasure

setEvaluationMeasure

setOptions

getOptions

getSearchSpec

getCapabilities

setUpEvaluator

buildClassifier

distributionForInstance

printFeatures

measureNumRules

enumerateMeasures

getMeasure

toString

getRevision

main