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Visible to Intel only — GUID: GUID-A512DD09-41D1-4FCE-8366-5B367FCC9EA0
BrownBoost Classifier
BrownBoost is a boosting classification algorithm. It is more robust to noisy data sets than other boosting classification algorithms [Freund99].
BrownBoost 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 a two-class BrownBoost classifier.
Training Stage
The model is trained using the Freund method [Freund01] as follows:
Calculate , where:
is an inverse error function,
is a target classification error of the algorithm defined as
is the error function,
is a hypothesis formulated by the i-th weak learner, ,
is the weight of the hypothesis.
Set initial prediction values: .
Set “remaining timing”: .
Do for until
With each feature vector and its label of positive weight, associate .
Call the weak learner with the distribution defined by normalizing to receive a hypothesis .
Solve the differential equation
with given boundary conditions and to find and such that either or , where is a given small constant needed to avoid degenerate cases.
Update the prediction values: .
Update “remaining time”: .
End do
The result of the model training is the array of M weak learners .
Prediction Stage
Given the BrownBoost classifier and r feature vectors , the problem is to calculate the final classification confidence, a number from the interval , using the rule:
Batch Processing
BrownBoost 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, a BrownBoost 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 BrownBoost classifier. The only training method supported so far is the Y. Freund’s method. |
nClasses |
2 |
The number of classes. |
weakLearnerTraining |
DEPRECATED: Pointer to an object of the weak learner training class USE INSTEAD: Pointer to an object of the classification stump training class |
DEPRECATED: Pointer to the training algorithm of the weak learner. By default, a stump weak learner is used. USE INSTEAD: Pointer to the classifier training algorithm. Be default, a classification stump with gini split criterion is used. |
weakLearnerPrediction |
DEPRECATED: Pointer to an object of the weak learner prediction class USE INSTEAD: Pointer to an object of the classification stump prediction class |
DEPRECATED: Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used. USE INSTEAD: Pointer to the classifier prediction algorithm. Be default, a classification stump with gini split criterion is used. |
accuracyThreshold |
0.01 |
BrownBoost training accuracy . |
maxIterations |
100 |
The maximal number of iterations for the BrownBoost algorithm. |
newtonRaphsonAccuracyThreshold |
Accuracy threshold of the Newton-Raphson method used underneath the BrownBoost algorithm. |
|
newtonRaphsonMaxIterations |
100 |
The maximal number of Newton-Raphson iterations in the algorithm. |
degenerateCasesThreshold |
The threshold used to avoid degenerate cases. |
Prediction
For a description of the input and output, refer to Classification Usage Model.
At the prediction stage, a BrownBoost 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 BrownBoost classifier. |
nClasses |
2 |
The number of classes. |
weakLearnerPrediction |
DEPRECATED: Pointer to an object of the weak learner prediction class USE INSTEAD: Pointer to an object of the classification stump prediction class |
DEPRECATED: Pointer to the prediction algorithm of the weak learner. By default, a stump weak learner is used. USE INSTEAD: Pointer to the classifier prediction algorithm. Be default, a classification stump with gini split criterion is used. |
accuracyThreshold |
0.01 |
BrownBoost training accuracy . |
Examples
C++ (CPU)
Batch Processing:
Python*
Batch Processing: