Multiclass 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 twoclass problem only. These
twoclass (or binary) classifiers can be turned into multiclass
classifiers by using different strategies, such as OneAgainstRest
or OneAgainstOne.
oneDAL implements a MultiClass Classifier using the
OneAgainstOne strategy.
Multiclass classifiers, such as SVM, are based on twoclass
classifiers, which are integral components of the models trained with
the corresponding multiclass classifier algorithms.
Details
Given
n
feature vectors
of size p
, the number of classes K
, and a vector of class labels
, where ,
the problem is to build a multiclass classifier using a twoclass
(binary) classifier, such as a twoclass SVM.Training Stage
The model is trained with the OneAgainstOne 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.
 Votebased method. If the binary classifier predicts the feature vector to be inith class, the number of votes for the class i is increased by one, otherwise the vote is given to the jth class. If two classes have equal numbers of votes, the class with the smallest index is selected.
Usage of Training Alternative
To build a Multiclass Classifier model using methods of the Model Builder class of Multiclass Classifier,
complete the following steps:
 Create a Multiclass Classifier model builder using a constructor with the required number of features and classes.
 Use thesetTwoClassClassifierModelmethod for each pair of classes to add the pretrained twoclass classifiers to the model. In the parameters to the method specify the classes’ indices and the pointer to the pretrained twoclass classifier for this pair of classes. You need to do this for each pair of classes, because the OneAgainstOne strategy is used.
 Use thegetModelmethod to get the trained Multiclass 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*
Python*
Batch Processing
Multiclass classifier follows the general workflow described in Classification Usage Model.
Training
At the training stage, a multiclass classifier has the following
parameters:
Parameter  Default Value  Description 

algorithmFPType  float  The floatingpoint type that the algorithm uses for intermediate
computations. Can be float or double . 
method  defaultDense  The computation method used by the multiclass classifier. The only
training method supported so far is OneAgainstOne. 
training  Pointer to an object of the SVM training class  Pointer to the training algorithm of the twoclass classifier. By default, the SVM twoclass classifier is used. 
nClasses  Not applicable  The number of classes. A required parameter. 
Prediction
At the prediction stage, a multiclass classifier has the following parameters:
Parameter  Method  Default Value  Description 

algorithmFPType  defaultDense or voteBased  float  The floatingpoint type that the algorithm uses for intermediate
computations. Can be float or double . 
pmethod  Not applicable  defaultDense  Available methods for multiclass classifier prediction stage:

tmethod  defaultDense or voteBased  training::oneAgainstOne  The computation method that was used to train the multiclass classifier model. 
prediction  defaultDense or voteBased  Pointer to an object of the SVM prediction class  Pointer to the prediction algorithm of the twoclass classifier. By default, the SVM twoclass 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  1.0e12  The prediction accuracy. 
resultsToEvaluate  voteBased  computeClassLabels  The 64bit 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 containing the results of the decision function
computed for all binary models when the 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: