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Multiclass Classification

In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes.

Currently, Apache Ignite ML support the most popular method of Multiclass classification known as One-vs-Rest.

One-vs-Rest strategy involves training a single classifier per class, with the samples of that class as positive samples and all other samples as negatives.

Internally it uses one dataset but with the different changed labels for each trained classifier. If you have N classes, the N classifiers will be trained to become a MultiClassModel.

MultiClassModel uses soft-margin technique to predict the real label. It means that the MultiClassModel returns the label of the class which is better suited for the predicted vector.

Example

To see how One-vs-Rest trainer parametrized by binary SVM classifier can be used in practice, try this example that is available on GitHub and delivered with every Apache Ignite distribution.

The preprocessed Glass dataset is from the UCI Machine Learning Repository.

There are 3 classes with labels: 1 (building_windows_float_processed), 3 (vehicle_windows_float_processed), 7 (headlamps) and feature names: 'Na-Sodium', 'Mg-Magnesium', 'Al-Aluminum', 'Ba-Barium', 'Fe-Iron'.

OneVsRestTrainer<SVMLinearClassificationModel> trainer
                    = new OneVsRestTrainer<>(new SVMLinearClassificationTrainer()
                    .withAmountOfIterations(20)
                    .withAmountOfLocIterations(50)
                    .withLambda(0.2)
                    .withSeed(1234L)
                );

MultiClassModel<SVMLinearClassificationModel> mdl = trainer.fit(
                    ignite,
                    dataCache,
                    new DummyVectorizer<Integer>().labeled(0)
                );

double prediction = mdl.predict(inputVector);