Multi-class Classifier
While some classification algorithms naturally permit the use of more
than two classes, some algorithms, such as Support Vector Machines
(SVM), are by nature solving a two-class problem only. These
two-class (or binary) classifiers can be turned into multi-class
classifiers by using different strategies, such as One-Against-Rest
or One-Against-One.
oneDAL implements a Multi-Class Classifier using the
One-Against-One strategy.
Multi-class classifiers, such as SVM, are based on two-class
classifiers, which are integral components of the models trained with
the corresponding multi-class classifier algorithms.
Details
Given
of size
, where
,
the problem is to build a multi-class classifier using a two-class
(binary) classifier, such as a two-class SVM.
n
feature vectors
p
, the number of classes K
, and a vector of class labels
Training Stage
The model is trained with the One-Against-One method that uses the
binary classification described in
[Hsu02] as follows:
For each pair of classes
, train a binary classifier, such
as SVM. The total number of such binary classifiers is
.
Prediction Stage
Given a new feature vector
, the classifier determines
the class to which the vector belongs.
oneDAL provides two methods for class label prediction:
- Wu method. According to the algorithm 2 for computation of the class probabilities described in [Wu04]. The library returns the index of the class with the largest probability.
- Vote-based method. If the binary classifier predicts the feature vector to be ini-th class, the number of votes for the class i is increased by one, otherwise the vote is given to the j-th class. If two classes have equal numbers of votes, the class with the smallest index is selected.
Usage of Training Alternative
To build a Multi-class Classifier model using methods of the Model Builder class of Multi-class Classifier,
complete the following steps:
- Create a Multi-class Classifier model builder using a constructor with the required number of features and classes.
- Use thesetTwoClassClassifierModelmethod for each pair of classes to add the pre-trained two-class classifiers to the model. In the parameters to the method specify the classes’ indices and the pointer to the pre-trained two-class classifier for this pair of classes. You need to do this for each pair of classes, because the One-Against-One strategy is used.
- Use thegetModelmethod to get the trained Multi-class Classifier model.
- Use thegetStatusmethod to check the status of the model building process. IfDAAL_NOTHROW_EXCEPTIONSmacros is defined, the status report contains the list of errors that describe the problems API encountered (in case of API runtime failure).
Examples
oneAPI C++
Batch Processing
C++ (CPU)
Batch Processing
Java*
Batch Processing
Multi-class classifier follows the general workflow described in Classification Usage Model.
Training
At the training stage, a multi-class 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 multi-class classifier. The only
training method supported so far is One-Against-One. |
training | Pointer to an object of the SVM training class | Pointer to the training algorithm of the two-class classifier. By default, the SVM two-class classifier is used. |
nClasses | Not applicable | The number of classes. A required parameter. |
Prediction
At the prediction stage, a multi-class classifier has the following parameters:
Parameter | Method | Default Value | Description |
---|---|---|---|
algorithmFPType | defaultDense or voteBased | float | The floating-point type that the algorithm uses for intermediate
computations. Can be float or double . |
pmethod | Not applicable | defaultDense | Available methods for multi-class classifier prediction stage:
|
tmethod | defaultDense or voteBased | training::oneAgainstOne | The computation method that was used to train the multi-class classifier model. |
prediction | defaultDense or voteBased | Pointer to an object of the SVM prediction class | Pointer to the prediction algorithm of the two-class classifier. By default, the SVM two-class classifier is used. |
nClasses | defaultDense or voteBased | Not applicable | The number of classes. A required parameter. |
maxIterations | defaultDense | 100 | The maximal number of iterations for the algorithm. |
accuracyThreshold | defaultDense | 1.0e-12 | The prediction accuracy. |
resultsToEvaluate | voteBased | computeClassLabels | The 64-bit integer flag that specifies which extra characteristics of the decision function to compute. Provide one of the following values to request a single characteristic
or use bitwise OR to request a combination of the characteristics:
|
Output
In addition to classifier output, multiclass classifier calculates the result described below.
Pass the
Result ID
as a parameter to the methods that access the result of your algorithm.
For more details, see Algorithms.Result ID | Result |
---|---|
decisionFunction | A numeric table of size
computeDecisionFunction option is enabled. |
If
resultsToEvaluate
does not contain computeDecisionFunction
, the result of decisionFunction
table is NULL
.By default, each numeric table of this result is an object of the
HomogenNumericTable
class,
but you can define the result as an object of any class derived from NumericTable
except for PackedSymmetricMatrix
and PackedTriangularMatrix
.Examples
Java*
There is no support for Java on GPU.
Batch Processing:
Python*
Batch Processing: