weka.classifiers.rules

Class DTNB

java.lang.Object

weka.classifiers.Classifier

weka.classifiers.rules.DecisionTable

weka.classifiers.rules.DTNB

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class DTNB
extends DecisionTable

Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the attributes into two disjoint subsets: one for the decision table, the other for naive Bayes. A forward selection search is used, where at each step, selected attributes are modeled by naive Bayes and the remainder by the decision table, and all attributes are modelled by the decision table initially. At each step, the algorithm also considers dropping an attribute entirely from the model.

For more information, see:

Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), ???-???, 2008.

BibTeX:

 @inproceedings{Hall2008,
    author = {Mark Hall and Eibe Frank},
    booktitle = {Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS)},
    pages = {318-319},
    publisher = {AAAI press},
    title = {Combining Naive Bayes and Decision Tables},
    year = {2008}
 }
 

Valid options are:

 -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.
 

Version:

$Revision: 6269 $

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}org), Eibe Frank (eibe{[at]}cs{[dot]}waikato{[dot]}ac{[dot]}nz)

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.rules.DecisionTable

EVAL_ACCURACY, EVAL_AUC, EVAL_DEFAULT, EVAL_MAE, EVAL_RMSE, TAGS_EVALUATION

Constructor Summary

Constructors

Constructor and Description
DTNB()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
java.util.Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
Capabilities	getCapabilities() Returns default capabilities of the classifier.
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.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
double	measurePercentAttsUsedByDT() Returns the number of rules
void	setOptions(java.lang.String[] options) Parses the options for this object.
void	setSearch(ASSearch search) Sets the search method to use
java.lang.String	toString() Returns a description of the classifier.

Methods inherited from class weka.classifiers.rules.DecisionTable

crossValTipText, displayRulesTipText, evaluationMeasureTipText, getCrossVal, getDisplayRules, getEvaluationMeasure, getUseIBk, measureNumRules, printFeatures, searchTipText, setCrossVal, setDisplayRules, setEvaluationMeasure, setUseIBk, useIBkTipText

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

Constructor Detail

DTNB

public DTNB()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Overrides:

globalInfo in class DecisionTable

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

Overrides:

getTechnicalInformation in class DecisionTable

Returns:

the technical information about this class

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Generates the classifier.

Overrides:

buildClassifier in class DecisionTable

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.

Overrides:

distributionForInstance in class DecisionTable

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if distribution can't be computed

toString

public java.lang.String toString()

Description copied from class: DecisionTable

Returns a description of the classifier.

Overrides:

toString in class DecisionTable

Returns:

a description of the classifier as a string.

measurePercentAttsUsedByDT

public double measurePercentAttsUsedByDT()

Returns the number of rules

Returns:

the number of rules

enumerateMeasures

public java.util.Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names

Specified by:

enumerateMeasures in interface AdditionalMeasureProducer

Overrides:

enumerateMeasures in class DecisionTable

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

Overrides:

getMeasure in class DecisionTable

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

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class DecisionTable

Returns:

the capabilities of this classifier

See Also:

Capabilities

setSearch

public void setSearch(ASSearch search)

Sets the search method to use

Overrides:

setSearch in class DecisionTable

Parameters:

search -

getSearch

public ASSearch getSearch()

Gets the current search method

Overrides:

getSearch in class DecisionTable

Returns:

the search method used

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class DecisionTable

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses the options for this object.

Valid options are:

 -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.
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class DecisionTable

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 DecisionTable

Returns:

an array of strings suitable for passing to setOptions

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class DecisionTable

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the command-line options