## Developer Guide and Reference

• 2021.3
• 06/28/2021
• Public Content
Contents

# 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:
1. 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.
2. Set initial prediction values: .
3. Set “remaining timing”: .
4. Do for until 1. With each feature vector and its label of positive weight, associate .
2. Call the weak learner with the distribution defined by normalizing Lmath:
W_i(x, y)
to receive a hypothesis .
3. 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.
4. Update the prediction values: .
5. 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
: 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.
: 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
: 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.
: 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
: 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.
: 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:
Java*
There is no support for Java on GPU.
Batch Processing:
Python*
Batch Processing:

#### Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.