jsat.classifiers.boosting

Class AdaBoostM1

java.lang.Object

jsat.classifiers.boosting.AdaBoostM1

All Implemented Interfaces:

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

public class AdaBoostM1
extends java.lang.Object
implements Classifier, Parameterized

Implementation of Experiments with a New Boosting Algorithm, by Yoav Freund&Robert E. Schapire.
This is the first AdaBoost algorithm presented in the paper, and the first boosting algorithm. Though not often mentioned, AdaBoost does support non binary classification tasks. However, for any k labels, the weak learner's error still needs to be better then 1/2, which is not an easy requirement to satisfy. For this reason, many use AdaBoostM1 by reducing k class classification problems to several 2 class problems.

Many Boosting methods, when given a binary classification task, reduce to having the same results as this class.

AdaBoost is often combined with OneVSAll to obtain better classification accuracy.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
AdaBoostM1(AdaBoostM1 toCopy)
AdaBoostM1(Classifier weakLearner, int maxIterations)

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
AdaBoostM1	clone()
int	getMaxIterations() Returns the maximum number of iterations used
java.util.List<Classifier>	getModels()
java.util.List<java.lang.Double>	getModelWeights()
Classifier	getWeakLearner() Returns the weak learner currently being used by this method.
void	setMaxIterations(int maxIterations) Sets the maximal number of boosting iterations that may be performed
void	setWeakLearner(Classifier weakLearner) Sets the weak learner used during training.
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

AdaBoostM1

public AdaBoostM1(Classifier weakLearner,
                  int maxIterations)

AdaBoostM1

public AdaBoostM1(AdaBoostM1 toCopy)

Method Detail

getMaxIterations

public int getMaxIterations()

Returns the maximum number of iterations used

Returns:

the maximum number of iterations used

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.

setMaxIterations

public void setMaxIterations(int maxIterations)

Sets the maximal number of boosting iterations that may be performed

Parameters:

maxIterations - the maximum number of iterations

getWeakLearner

public Classifier getWeakLearner()

Returns the weak learner currently being used by this method.

Returns:

the weak learner currently being used by this method.

setWeakLearner

public void setWeakLearner(Classifier weakLearner)

Sets the weak learner used during training.

Parameters:

weakLearner - the weak learner to use

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 AdaBoostM1 clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class java.lang.Object