Class LWLIntervalEstimator

  • All Implemented Interfaces:
    Serializable, Cloneable, weka.classifiers.Classifier, weka.classifiers.IntervalEstimator, ThreadSafeClassifier, weka.classifiers.UpdateableClassifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

    public class LWLIntervalEstimator
    extends LWLSynchro
    implements weka.classifiers.IntervalEstimator
    Locally weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler.
    Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression).

    For more info, see

    Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003.

    C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..

        author = {Eibe Frank and Mark Hall and Bernhard Pfahringer},
        booktitle = {19th Conference in Uncertainty in Artificial Intelligence},
        pages = {249-256},
        publisher = {Morgan Kaufmann},
        title = {Locally Weighted Naive Bayes},
        year = {2003}
        author = {C. Atkeson and A. Moore and S. Schaal},
        journal = {AI Review},
        title = {Locally weighted learning},
        year = {1996}

    Valid options are:

      The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
     -K <number of neighbours>
      Set the number of neighbours used to set the kernel bandwidth.
      (default all)
     -U <number of weighting method>
      Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
      2=Tricube, 3=Inverse, 4=Gaussian.
      (default 0 = Linear)
      If set, classifier is run in debug mode and
      may output additional info to the console
      Full name of base classifier.
      (default: weka.classifiers.trees.DecisionStump)
     Options specific to classifier weka.classifiers.trees.DecisionStump:
      If set, classifier is run in debug mode and
      may output additional info to the console
    Len Trigg (, Eibe Frank (, Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
    See Also:
    Serialized Form
    • Field Summary

      • Fields inherited from class weka.classifiers.lazy.LWL

        CONSTANT, EPANECHNIKOV, GAUSS, INVERSE, LINEAR, m_kNN, m_NNSearch, m_Train, m_UseAllK, m_WeightKernel, m_ZeroR, TRICUBE
      • Fields inherited from class weka.classifiers.SingleClassifierEnhancer

      • Fields inherited from class weka.classifiers.AbstractClassifier

        BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT
    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      String getRevision()
      Returns the revision string.
      static void main​(String[] args)
      Main method for executing this classifier.
      double[][] predictIntervals​(weka.core.Instance inst, double confidenceLevel)
      Returns an N * 2 array, where N is the number of prediction intervals.
      void setClassifier​(weka.classifiers.Classifier value)
      Set the base learner, which must implement IntervalEstimator.
      • Methods inherited from class weka.classifiers.lazy.LWL

        buildClassifier, enumerateMeasures, getCapabilities, getKNN, getMeasure, getNearestNeighbourSearchAlgorithm, getTechnicalInformation, getWeightingKernel, globalInfo, KNNTipText, nearestNeighbourSearchAlgorithmTipText, setKNN, setNearestNeighbourSearchAlgorithm, setWeightingKernel, updateClassifier, weightingKernelTipText
      • Methods inherited from class weka.classifiers.SingleClassifierEnhancer

        classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution
      • Methods inherited from class weka.classifiers.AbstractClassifier

        batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
      • Methods inherited from interface weka.classifiers.Classifier

        buildClassifier, classifyInstance, getCapabilities
    • Constructor Detail

      • LWLIntervalEstimator

        public LWLIntervalEstimator()
    • Method Detail

      • setClassifier

        public void setClassifier​(weka.classifiers.Classifier value)
        Set the base learner, which must implement IntervalEstimator.
        setClassifier in class weka.classifiers.SingleClassifierEnhancer
        value - the classifier to use.
        See Also:
      • predictIntervals

        public double[][] predictIntervals​(weka.core.Instance inst,
                                           double confidenceLevel)
                                    throws Exception
        Returns an N * 2 array, where N is the number of prediction intervals. In each row, the first element contains the lower boundary of the corresponding prediction interval and the second element the upper boundary.
        Specified by:
        predictIntervals in interface weka.classifiers.IntervalEstimator
        inst - the instance to make the prediction for.
        confidenceLevel - the percentage of cases that the interval should cover.
        an array of prediction intervals
        Exception - if the intervals can't be computed
      • getRevision

        public String getRevision()
        Returns the revision string.
        Specified by:
        getRevision in interface weka.core.RevisionHandler
        getRevision in class LWLSynchro
        the revision
      • main

        public static void main​(String[] args)
        Main method for executing this classifier.
        args - the options, use -h to display all