weka.classifiers.lazy
Class LWLIntervalEstimator

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.SingleClassifierEnhancer
          weka.classifiers.lazy.LWL
              weka.classifiers.lazy.LWLSynchro
                  weka.classifiers.lazy.LWLIntervalEstimator

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.classifiers.IntervalEstimator, weka.classifiers.UpdateableClassifier, weka.core.CapabilitiesHandler, 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..

BibTeX:

 @inproceedings{Frank2003,
    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}
 }

 @article{Atkeson1996,
    author = {C. Atkeson and A. Moore and S. Schaal},
    journal = {AI Review},
    title = {Locally weighted learning},
    year = {1996}
 }
 

Valid options are:

 -A
  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)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:

$Revision: 4521 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz), 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.LWLSynchro

m_NoUpdate

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

m_Classifier

Fields inherited from class weka.classifiers.AbstractClassifier

m_Debug

Constructor Summary

LWLIntervalEstimator()

Method Summary

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.LWLSynchro

build, defaultClassifierString, distributionForInstance, getNoUpdate, getOptions, listOptions, noUpdateTipText, setNoUpdate, setOptions, toString

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, getClassifier, getClassifierSpec

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

LWLIntervalEstimator

public LWLIntervalEstimator()

Method Detail

setClassifier

public void setClassifier(weka.classifiers.Classifier value)

Set the base learner, which must implement IntervalEstimator.

Overrides:

setClassifier in class weka.classifiers.SingleClassifierEnhancer

Parameters:

value - the classifier to use.

See Also:

IntervalEstimator

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

Parameters:

inst - the instance to make the prediction for.

confidenceLevel - the percentage of cases that the interval should cover.

Returns:

an array of prediction intervals

Throws:

Exception - if the intervals can't be computed

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class LWLSynchro

Returns:

the revision

main

public static void main(String[] args)

Main method for executing this classifier.

Parameters:

args - the options, use -h to display all