Package ai.libs.jaicore.ml.weka.classification.timeseries.learner.ensemble

Class EnsembleProvider

  • public class EnsembleProvider
extends java.lang.Object
    Class statically providing preconfigured ensembles as commonly used in TSC implementations.

    • Method Summary

      All Methods Static Methods Concrete Methods 
      Modifier and Type Method Description
      static weka.classifiers.Classifier provideCAWPEEnsembleModel​(int seed, int numFolds)
      Initializes the CAWPE ensemble model consisting of five classifiers (SMO, KNN, J48, Logistic and MLP) using a majority voting strategy.
      static weka.classifiers.Classifier provideHIVECOTEEnsembleModel​(long seed)
      Initializes the HIVE COTE ensemble consisting of 7 classifiers using a majority voting strategy as described in J.

      • Methods inherited from class java.lang.Object

        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Method Detail

      • provideCAWPEEnsembleModel

        public static weka.classifiers.Classifier provideCAWPEEnsembleModel​(int seed,
                                                                    int numFolds)
                                                             throws java.lang.Exception
        Initializes the CAWPE ensemble model consisting of five classifiers (SMO, KNN, J48, Logistic and MLP) using a majority voting strategy. The ensemble uses Weka classifiers. It refers to "Heterogeneous ensemble of standard classification algorithms" (HESCA) as described in Lines, Jason & Taylor, Sarah & Bagnall, Anthony. (2018). Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles. ACM Transactions on Knowledge Discovery from Data. 12. 1-35. 10.1145/3182382.
        Parameters:
        seed - Seed used within the classifiers and the majority confidence voting scheme
        numFolds - Number of folds used within the determination of the classifier weights for the MajorityConfidenceVote
        Returns:
        Returns an initialized (but untrained) ensemble model.
        Throws:
        java.lang.Exception - Thrown when the initialization has failed

      • provideHIVECOTEEnsembleModel

        public static weka.classifiers.Classifier provideHIVECOTEEnsembleModel​(long seed)
        Initializes the HIVE COTE ensemble consisting of 7 classifiers using a majority voting strategy as described in J. Lines, S. Taylor and A. Bagnall, "HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification," 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, 2016, pp. 1041-1046. doi: 10.1109/ICDM.2016.0133.
        Parameters:
        seed - Seed used within the classifiers and the majority confidence voting scheme
        numFolds - Number of folds used within the determination of the classifier weights for the MajorityConfidenceVote
        Returns:
        Returns the initialized (but untrained) HIVE COTE ensemble model.