Class HeterogeneousEnsembleBlastFadingFactors

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

    public class HeterogeneousEnsembleBlastFadingFactors
    extends HeterogeneousEnsembleAbstract
    implements MultiClassClassifier
    BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factors

    Given a set of (heterogeneous) classifiers, BLAST builds an ensemble, and determines the weights of all ensemble members based on their performance on recent observed instances. This implementation uses fading factors, to emphasize the importance of recent predictions and fade away old predictions.

    J. N. van Rijn, G. Holmes, B. Pfahringer, J. Vanschoren. Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. In 2015 IEEE International Conference on Data Mining, pages 1003-1008. IEEE, 2015.

    Parameters:

    • -f : Fading factor
    • -b : Comma-separated string of classifiers
    • -g : Grace period (1 = optimal)
    • -k : Number of active classifiers
    Version:
    $Revision: 1 $
    Author:
    Jan N. van Rijn (j.n.van.rijn@liacs.leidenuniv.nl)
    See Also:
    Serialized Form
    • Constructor Detail

      • HeterogeneousEnsembleBlastFadingFactors

        public HeterogeneousEnsembleBlastFadingFactors()
    • 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