jsat.classifiers.boosting

Class ArcX4

java.lang.Object

jsat.classifiers.boosting.ArcX4

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Parameterized

public class ArcX4
extends java.lang.Object
implements Classifier, Parameterized

Arc-x4 is a ensemble-classifier that performs re-weighting of the data points based on the total number of errors that have occurred for the data point.

See: Breiman, L. (1998). Arcing Classifiers. The Annals of Statistics, 26(3), 801–824.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ArcX4(Classifier weakLearner, int iterations) Creates a new Arc-X4 classifier

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
ArcX4	clone()
double	getCoefficient() Returns the coefficient use when re-weighting
double	getExponent() Returns the exponent used when re-weighting
int	getIterations() Returns the number of iterations to learn
Classifier	getWeakLearner() Returns the weak learner used
void	setCoefficient(double coef) Weights are updated as 1+coef*errorsexpo.
void	setExponent(double expo) Weights are updated as 1+coef*errorsexpo.
void	setIterations(int iterations) Sets the number of iterations to perform
void	setWeakLearner(Classifier weakLearner) Sets the weak learner used at each iteration of learning
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object

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

Methods inherited from interface jsat.classifiers.Classifier

train

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Constructor Detail

ArcX4

public ArcX4(Classifier weakLearner,
             int iterations)

Creates a new Arc-X4 classifier

Parameters:

weakLearner - the weak learner to use

iterations - the number of iterations to perform

Method Detail

setWeakLearner

public void setWeakLearner(Classifier weakLearner)

Sets the weak learner used at each iteration of learning

Parameters:

weakLearner - the weak learner to use

getWeakLearner

public Classifier getWeakLearner()

Returns the weak learner used

Returns:

the weak learner used

setIterations

public void setIterations(int iterations)

Sets the number of iterations to perform

Parameters:

iterations - the number of iterations to do

getIterations

public int getIterations()

Returns the number of iterations to learn

Returns:

the number of iterations to learn

setCoefficient

public void setCoefficient(double coef)

Weights are updated as 1+coef*errors^expo. This sets the coefficient used to update the errors

Parameters:

coef - the multiplicative factor on the errors in weight construction

getCoefficient

public double getCoefficient()

Returns the coefficient use when re-weighting

Returns:

the coefficient use when re-weighting

setExponent

public void setExponent(double expo)

Weights are updated as 1+coef*errors^expo. This sets the exponent used to update the errors

Parameters:

expo - the exponent to use

getExponent

public double getExponent()

Returns the exponent used when re-weighting

Returns:

the exponent used when re-weighting

classify

public CategoricalResults classify(DataPoint data)

Description copied from interface: Classifier

Performs classification on the given data point.

Specified by:

classify in interface Classifier

Parameters:

data - the data point to classify

Returns:

the results of the classification.

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Classifier

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Specified by:

supportsWeightedData in interface Classifier

Returns:

true if the model supports weighted data, false otherwise

clone

public ArcX4 clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class java.lang.Object