weka.classifiers.rules

Class OneR

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.rules.OneR

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Sourcable, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class OneR
extends AbstractClassifier
implements TechnicalInformationHandler, Sourcable

Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes. For more information, see:

R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.

BibTeX:

 @article{Holte1993,
    author = {R.C. Holte},
    journal = {Machine Learning},
    pages = {63-91},
    title = {Very simple classification rules perform well on most commonly used datasets},
    volume = {11},
    year = {1993}
 }
 

Valid options are:

 -B <minimum bucket size>
  The minimum number of objects in a bucket (default: 6).
 

Version:

$Revision: 10153 $

Author:

Ian H. Witten (ihw@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
OneR()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances instances) Generates the classifier.
double	classifyInstance(Instance inst) Classifies a given instance.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getMinBucketSize() Get the value of minBucketSize.
java.lang.String[]	getOptions() Gets the current settings of the OneR classifier.
java.lang.String	getRevision() Returns the revision string.
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<Option>	listOptions() Returns an enumeration describing the available options..
static void	main(java.lang.String[] argv) Main method for testing this class
java.lang.String	minBucketSizeTipText() Returns the tip text for this property
weka.classifiers.rules.OneR.OneRRule	newNominalRule(Attribute attr, Instances data, int[] missingValueCounts) Create a rule branching on this nominal attribute.
weka.classifiers.rules.OneR.OneRRule	newNumericRule(Attribute attr, Instances data, int[] missingValueCounts) Create a rule branching on this numeric attribute
weka.classifiers.rules.OneR.OneRRule	newRule(Attribute attr, Instances data) Create a rule branching on this attribute.
void	setMinBucketSize(int v) Set the value of minBucketSize.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toSource(java.lang.String className) Returns a string that describes the classifier as source.
java.lang.String	toString() Returns a description of the classifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

OneR

public OneR()

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

classifyInstance

public double classifyInstance(Instance inst)
                        throws java.lang.Exception

Classifies a given instance.

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

Parameters:

inst - the instance to be classified

Returns:

the classification of the instance

Throws:

java.lang.Exception - if an error occurred during the prediction

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 instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

instances - the instances to be used for building the classifier

Throws:

java.lang.Exception - if the classifier can't be built successfully

newRule

public weka.classifiers.rules.OneR.OneRRule newRule(Attribute attr,
                                                    Instances data)
                                             throws java.lang.Exception

Create a rule branching on this attribute.

Parameters:

attr - the attribute to branch on

data - the data to be used for creating the rule

Returns:

the generated rule

Throws:

java.lang.Exception - if the rule can't be built successfully

newNominalRule

public weka.classifiers.rules.OneR.OneRRule newNominalRule(Attribute attr,
                                                           Instances data,
                                                           int[] missingValueCounts)
                                                    throws java.lang.Exception

Create a rule branching on this nominal attribute.

Parameters:

attr - the attribute to branch on

data - the data to be used for creating the rule

missingValueCounts - to be filled in

Returns:

the generated rule

Throws:

java.lang.Exception - if the rule can't be built successfully

newNumericRule

public weka.classifiers.rules.OneR.OneRRule newNumericRule(Attribute attr,
                                                           Instances data,
                                                           int[] missingValueCounts)
                                                    throws java.lang.Exception

Create a rule branching on this numeric attribute

Parameters:

attr - the attribute to branch on

data - the data to be used for creating the rule

missingValueCounts - to be filled in

Returns:

the generated rule

Throws:

java.lang.Exception - if the rule can't be built successfully

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.

setOptions

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

Parses a given list of options.

Valid options are:

 -B <minimum bucket size>
  The minimum number of objects in a bucket (default: 6).
 

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 OneR classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

toSource

public java.lang.String toSource(java.lang.String className)
                          throws java.lang.Exception

Returns a string that describes the classifier as source. The classifier will be contained in a class with the given name (there may be auxiliary classes), and will contain a method with the signature:

 
 public static double classify(Object[] i);
 
 

where the array i contains elements that are either Double, String, with missing values represented as null. The generated code is public domain and comes with no warranty.

Specified by:

toSource in interface Sourcable

Parameters:

className - the name that should be given to the source class.

Returns:

the object source described by a string

Throws:

java.lang.Exception - if the souce can't be computed

toString

public java.lang.String toString()

Returns a description of the classifier

Overrides:

toString in class java.lang.Object

Returns:

a string representation of the classifier

minBucketSizeTipText

public java.lang.String minBucketSizeTipText()

Returns the tip text for this property

Returns:

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

getMinBucketSize

public int getMinBucketSize()

Get the value of minBucketSize.

Returns:

Value of minBucketSize.

setMinBucketSize

public void setMinBucketSize(int v)

Set the value of minBucketSize.

Parameters:

v - Value to assign to minBucketSize.

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 commandline options