weka.classifiers
Class DecisionTable

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.DistributionClassifier
              |
              +--weka.classifiers.DecisionTable

All Implemented Interfaces:

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

public class DecisionTable

extends DistributionClassifier

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.

Author:

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

See Also:

Serialized Form

Inner 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.

Constructor Summary

DecisionTable()
Constructor for a DecisionTable

Method Summary

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
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.util.Enumeration	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
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.

Methods inherited from class weka.classifiers.DistributionClassifier

classifyInstance

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

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

Constructor Detail

DecisionTable

public DecisionTable()

Constructor for a DecisionTable

Method Detail

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

Returns:

an enumeration of all the available options

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

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

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

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

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

Returns:

an array of strings suitable for passing to setOptions

buildClassifier

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

Generates the classifier.

Overrides:

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 DistributionClassifier

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:

measureName - 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.

main

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

Main method for testing this class.

Parameters:

argv - the command-line options