AdaBoost.h File Reference

This class contains the AdaBoost classifier. AdaBoost (Adaptive Boosting) is a powerful classifier that works well on both basic and more complex recognition problems. More...

#include "../../CoreModules/Classifier.h"
#include "AdaBoostClassModel.h"
#include "WeakClassifiers/DecisionStump.h"
#include "WeakClassifiers/RadialBasisFunction.h"

Go to the source code of this file.

Classes
class	AdaBoost

Detailed Description

This class contains the AdaBoost classifier. AdaBoost (Adaptive Boosting) is a powerful classifier that works well on both basic and more complex recognition problems.

Author

Nicholas Gillian ngill.nosp@m.ian@.nosp@m.media.nosp@m..mit.nosp@m..edu

Version

1.0

AdaBoost works by creating a highly accurate classifier by combining many relatively weak and inaccurate classifiers. AdaBoost therefore acts as a meta algorithm, which allows you to use it as a wrapper for other classifiers. In the GRT, these classifiers are called Weak Classifiers such as a GRT::DecisionStump (which is just one node of a DecisionTree). AdaBoost is adaptive in the sense that subsequent classifiers added at each round of boosting are tweaked in favor of those instances misclassified by previous classifiers. The default number of boosting rounds for AdaBoost is 20, however this can easily be set using the GRT::AdaBoost::setNumBoostingIterations(UINT numBoostingIterations) function or via the AdaBoost constructor.

Remarks

This implementation is based on Schapire, Robert E., and Yoav Freund. Boosting: Foundations and Algorithms. MIT Press, 2012.

Note

There are two classification modes, GRT::AdaBoost::PredictionMethods (MAX_POSITIVE_VALE and MAX_VALUE), which can give different classification results so you should experiment with each mode to achieve the best results for your classification task.

Definition in file AdaBoost.h.