jsat.classifiers.boosting

Class SAMME

java.lang.Object

jsat.classifiers.boosting.SAMME

All Implemented Interfaces:

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

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

This is an implementation of the Multi-Class AdaBoost method SAMME (Stagewise Additive Modeling using a Multi-Class Exponential loss function), presented in Multi-class AdaBoost by Ji Zhu, Saharon Rosset, Hui Zou,&Trevor Hasstie

This algorithm reduces to AdaBoostM1 for binary classification problems. Its often performs better for k class classification problems, and has a weaker requirement of besting 1/k accuracy for any k instead of 1/2.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
SAMME(Classifier weakLearner, int maxIterations)

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
SAMME	clone()
java.util.List<Classifier>	getModels()
java.util.List<java.lang.Double>	getModelWeights()
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

SAMME

public SAMME(Classifier weakLearner,
             int maxIterations)

Method Detail

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.

getModels

public java.util.List<Classifier> getModels()

Returns:

a list of the models that are in this ensemble.

getModelWeights

public java.util.List<java.lang.Double> getModelWeights()

Returns:

a list of the models weights that are in this ensemble.

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 SAMME clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class java.lang.Object