weka.classifiers.rules
Class DecisionTable

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

All Implemented Interfaces:

AdditionalMeasureProducer, java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

public class DecisionTable

extends Classifier

implements OptionHandler, WeightedInstancesHandler, AdditionalMeasureProducer

Class for building and using a simple decision table majority classifier. For more information see:

Kohavi R. (1995). The Power of Decision Tables. In Proc European Conference on Machine Learning.

Valid options are:

-S num
Number of fully expanded non improving subsets to consider before terminating a best first search. (Default = 5)

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

-I
Use nearest neighbour instead of global table majority.

-R
Prints the decision table.

Version:

$Revision: 1.27 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Serialized Form

Nested Class Summary

class	DecisionTable.hashKey Class providing keys to the hash table
class	DecisionTable.Link Class for a node in a linked list.
class	DecisionTable.LinkedList Class for handling a linked list.

Field Summary

private boolean	m_classIsNominal Class is nominal
private int	m_CVFolds Number of folds for cross validating feature sets
private boolean	m_debug Output debug info
private int[]	m_decisionFeatures Holds the final feature set
private Remove	m_delTransform Filter used to remove columns discarded by feature selection
private boolean	m_displayRules Display Rules
private Filter	m_disTransform Discretization filter
private java.util.Hashtable	m_entries The hashtable used to hold training instances
private IBk	m_ibk IB1 used to classify non matching instances rather than majority class
private double	m_majority Holds the majority class
private int	m_maxStale Maximum number of fully expanded non improving subsets for a best first search.
private int	m_numAttributes The number of attributes in the dataset
private int	m_numInstances The number of instances in the dataset
private java.util.Random	m_rr Random numbers for use in cross validation
private Instances	m_theInstances Holds the training instances
private boolean	m_useIBk Use the IBk classifier rather than majority class

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

DecisionTable()
Constructor for a DecisionTable

Method Summary

private void	best_first() Does a best first search
void	buildClassifier(Instances data) Generates the classifier.
(package private) double	classifyFoldCV(Instances fold, int[] fs) Calculates the accuracy on a test fold for internal cross validation of feature sets
(package private) double	classifyInstanceLeaveOneOut(Instance instance, double[] instA) Classifies an instance for internal leave one out cross validation of feature sets
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	enumerateMeasures() Returns an enumeration of the additional measure names
private double	estimateAccuracy(java.util.BitSet feature_set, int num_atts) Evaluates a feature subset by cross validation
int	getCrossVal() Gets the number of folds for cross validation
boolean	getDisplayRules() Gets whether rules are being printed
int	getMaxStale() Gets the number of non improving decision tables
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure
java.lang.String[]	getOptions() Gets the current settings of the classifier.
boolean	getUseIBk() Gets whether IBk is being used instead of the majority class
java.lang.String	globalInfo() Returns a string describing classifier
private void	insertIntoTable(Instance inst, double[] instA) Inserts an instance into the hash table
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	maxStaleTipText() Returns the tip text for this property
double	measureNumRules() Returns the number of rules
java.lang.String	printFeatures() Returns a string description of the features selected
private java.lang.String	printSub(java.util.BitSet sub) Returns a String representation of a feature subset
protected void	resetOptions() Resets the options.
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	setMaxStale(int stale) Sets the number of non improving decision tables to consider before abandoning the search.
void	setOptions(java.lang.String[] options) Parses the options for this object.
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 weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_entries

private java.util.Hashtable m_entries

The hashtable used to hold training instances

m_decisionFeatures

private int[] m_decisionFeatures

Holds the final feature set

m_disTransform

private Filter m_disTransform

Discretization filter

m_delTransform

private Remove m_delTransform

Filter used to remove columns discarded by feature selection

m_ibk

private IBk m_ibk

IB1 used to classify non matching instances rather than majority class

m_theInstances

private Instances m_theInstances

Holds the training instances

m_numAttributes

private int m_numAttributes

The number of attributes in the dataset

m_numInstances

private int m_numInstances

The number of instances in the dataset

m_classIsNominal

private boolean m_classIsNominal

Class is nominal

m_debug

private boolean m_debug

Output debug info

m_useIBk

private boolean m_useIBk

Use the IBk classifier rather than majority class

m_displayRules

private boolean m_displayRules

Display Rules

m_maxStale

private int m_maxStale

Maximum number of fully expanded non improving subsets for a best first search.

m_CVFolds

private int m_CVFolds

Number of folds for cross validating feature sets

m_rr

private java.util.Random m_rr

Random numbers for use in cross validation

m_majority

private double m_majority

Holds the majority class

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

insertIntoTable

private void insertIntoTable(Instance inst,
                             double[] instA)
                      throws java.lang.Exception

Inserts an instance into the hash table

Parameters:

inst - instance to be inserted

Throws:

java.lang.Exception - if the instance can't be inserted

classifyInstanceLeaveOneOut

double classifyInstanceLeaveOneOut(Instance instance,
                                   double[] instA)
                             throws java.lang.Exception

Classifies an instance for internal leave one out cross validation of feature sets

Parameters:

instance - instance to be "left out" and classified

instA - feature values of the selected features for the instance

Returns:

the classification of the instance

Throws:

java.lang.Exception

classifyFoldCV

double classifyFoldCV(Instances fold,
                      int[] fs)
                throws java.lang.Exception

Calculates the accuracy on a test fold for internal cross validation of feature sets

Parameters:

fold - set of instances to be "left out" and classified

fs - currently selected feature set

Returns:

the accuracy for the fold

Throws:

java.lang.Exception

estimateAccuracy

private double estimateAccuracy(java.util.BitSet feature_set,
                                int num_atts)
                         throws java.lang.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:

java.lang.Exception - if subset can't be evaluated

printSub

private java.lang.String printSub(java.util.BitSet sub)

Returns a String representation of a feature subset

Parameters:

sub - BitSet representation of a subset

Returns:

String containing subset

best_first

private void best_first()
                 throws java.lang.Exception

Does a best first search

Throws:

java.lang.Exception

resetOptions

protected void resetOptions()

Resets the options.

listOptions

public java.util.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 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

maxStaleTipText

public java.lang.String maxStaleTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMaxStale

public void setMaxStale(int stale)

Sets the number of non improving decision tables to consider before abandoning the search.

Parameters:

stale - the number of nodes

getMaxStale

public int getMaxStale()

Gets the number of non improving decision tables

Returns:

the number of non improving decision tables

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

setOptions

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

Parses the options for this object. Valid options are:

-S num
Number of fully expanded non improving subsets to consider before terminating a best first search. (Default = 5)

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

-I
Use nearest neighbour instead of global table majority.

-R
Prints the decision table.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class Classifier

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 Classifier

Returns:

an array of strings suitable for passing to setOptions

buildClassifier

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

Generates the classifier.

Specified by:

buildClassifier in class 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.

Overrides:

distributionForInstance in class Classifier

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 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.

Returns:

a description of the classifier as a string.

main

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

Main method for testing this class.

Parameters:

argv - the command-line options