Class LearnNSE

  • All Implemented Interfaces:
    Configurable, Serializable, CapabilitiesHandler, Classifier, MultiClassClassifier, AWTRenderable, Learner<Example<Instance>>, MOAObject, OptionHandler

    public class LearnNSE
    extends AbstractClassifier
    implements MultiClassClassifier

    Ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. It learns from consecutive batches of data that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift.

    Based on: Ryan Elwell and Robi Polikar. Incremental learning of concept drift in non-stationary environments. IEEE Transactions on Neural Networks, 22(10):1517-1531, October 2011. ISSN 1045-9227. URL http://dx.doi.org/10.1109/TNN.2011.2160459

    Author:
    Paulo Gonçalves (paulogoncalves@recife.ifpe.edu.br), Dariusz Brzezinski
    See Also:
    Serialized Form
    • Constructor Detail

      • LearnNSE

        public LearnNSE()
    • Method Detail

      • resetLearningImpl

        public void resetLearningImpl()
        Description copied from class: AbstractClassifier
        Resets this classifier. It must be similar to starting a new classifier from scratch.

        The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.
        Specified by:
        resetLearningImpl in class AbstractClassifier
      • trainOnInstanceImpl

        public void trainOnInstanceImpl​(Instance inst)
        Description copied from class: AbstractClassifier
        Trains this classifier incrementally using the given instance.

        The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.
        Specified by:
        trainOnInstanceImpl in class AbstractClassifier
        Parameters:
        inst - the instance to be used for training
      • isRandomizable

        public boolean isRandomizable()
        Description copied from interface: Learner
        Gets whether this learner needs a random seed. Examples of methods that needs a random seed are bagging and boosting.
        Specified by:
        isRandomizable in interface Learner<Example<Instance>>
        Returns:
        true if the learner needs a random seed.
      • getVotesForInstance

        public double[] getVotesForInstance​(Instance inst)
        Description copied from interface: Classifier
        Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements must be all zero.
        Specified by:
        getVotesForInstance in interface Classifier
        Specified by:
        getVotesForInstance in class AbstractClassifier
        Parameters:
        inst - the instance to be classified
        Returns:
        an array containing the estimated membership probabilities of the test instance in each class
      • getModelDescription

        public void getModelDescription​(StringBuilder out,
                                        int indent)
        Description copied from class: AbstractClassifier
        Returns a string representation of the model.
        Specified by:
        getModelDescription in class AbstractClassifier
        Parameters:
        out - the stringbuilder to add the description
        indent - the number of characters to indent
      • getModelMeasurementsImpl

        protected Measurement[] getModelMeasurementsImpl()
        Description copied from class: AbstractClassifier
        Gets the current measurements of this classifier.

        The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. Note that this will produce compiler errors if not overridden.
        Specified by:
        getModelMeasurementsImpl in class AbstractClassifier
        Returns:
        an array of measurements to be used in evaluation tasks