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AdaBoost Classifier
AdaBoost (short for “Adaptive Boosting”) is a popular boosting classification algorithm. AdaBoost algorithm performs well on a variety of data sets except some noisy data [Freund99].
AdaBoost is a binary classifier. For a multi-class case, use Multi-class Classifier framework of the library.
Details
Given n feature vectors of size p and a vector of class labels , where describes the class to which the feature vector belongs, and a weak learner algorithm, the problem is to build an AdaBoost classifier.
Training Stage
The following scheme shows the major steps of the algorithm:
Initialize weights for .
For :
Train the weak learner using weights
Choose a confidence value .
Update , where is a normalization factor.
Output the final hypothesis:
Prediction Stage
Given the AdaBoost classifier and r feature vectors , the problem is to calculate the final class:
Batch Processing
AdaBoost classifier follows the general workflow described in Classification Usage Model.
Training
For a description of the input and output, refer to Classification Usage Model.
At the training stage, an AdaBoost classifier has the following parameters:
Parameter |
Default Value |
Description |
---|---|---|
algorithmFPType |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
method |
defaultDense |
The computation method used by the AdaBoost classifier. The only training method supported so far is the Y. Freund’s method. |
weakLearnerTraining |
Pointer to an object of the stump training class |
Pointer to the training algorithm of the weak learner. By default, a stump weak learner is used. |
weakLearnerPrediction |
Pointer to an object of the stump prediction class |
Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used. |
accuracyThreshold |
0.01 |
AdaBoost training accuracy. |
maxIterations |
100 |
The maximal number of iterations for the algorithm. |
Prediction
For a description of the input and output, refer to Classification Usage Model.
At the prediction stage, an AdaBoost classifier has the following parameters:
Parameter |
Default Value |
Description |
---|---|---|
algorithmFPType |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
method |
defaultDense |
Performance-oriented computation method, the only method supported by the AdaBoost classifier at the prediction stage. |
weakLearnerPrediction |
Pointer to an object of the stump prediction class |
Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used. |
Examples
C++ (CPU)
Batch Processing:
Python*